Data processing method and related device

Abstract

The embodiment of the invention discloses a data processing method and a related device, on one hand, during analysis, a plurality of to-be-analyzed objects are aggregated based on an interaction relationship, and the to-be-analyzed objects with relatively close interaction relationships are aggregated into the same object community; therefore, when the interaction closeness degree between the plurality of to-be-analyzed objects and other to-be-analyzed objects in the object community to which the plurality of to-be-analyzed objects belong is analyzed, relatively equal and balanced community interaction parameters can be obtained. On the other hand, the computer equipment can further combine the community scale of the community to which the object to be analyzed belongs, and determine the interaction parameter based on the community interaction parameter, so that the interaction parameter can fuse the influence of the community scale on the interaction closeness degree; and the close degree of interaction between the to-be-analyzed object and other to-be-analyzed objects in the plurality of to-be-analyzed objects can be accurately represented.

Description

Technical Field

[0001] The present application relates to the technical field, and in particular to a testing method and related devices. Background Art

[0002] Applications are an inseparable part of people's lives today. They may be used in all aspects of life, such as using payment apps to pay for purchases, using game apps to play with friends during leisure time, etc. In order to increase the usage and promotion of applications, applications usually launch a variety of activities in the software, and application users can complete the activities by interacting with other users in the application.

[0003] Therefore, in order to increase activity participation and make the application more frequently used, in related technologies, the software party usually identifies the user groups with more frequent and close interactions among the software users, and launches specific activities for this group, so as to increase the number of interactions and the intensity of interactions in the activities as a whole.

[0004] However, when determining the interaction intensity of users, the related technology conducts analysis on the user group consisting of all users corresponding to the application. The analysis granularity is too large, and it is difficult to obtain a more accurate interaction intensity corresponding to each user when using the application. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a data processing method, which can perform a more accurate and reasonable analysis of the interaction closeness of the object to be analyzed in combination with the community size of the object community in which the object to be analyzed is located.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] In a first aspect, an embodiment of the present application discloses a data processing method, the method comprising:

[0008] Acquire a set of objects to be analyzed, wherein the set to be analyzed includes a plurality of objects to be analyzed;

[0009] Based on the interaction relationship between the multiple objects to be analyzed, the multiple objects to be analyzed are aggregated to generate multiple object communities corresponding to the set of objects to be analyzed, the interaction closeness between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the interaction closeness between the objects to be analyzed outside the target community and the target objects to be analyzed, the target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community;

[0010] According to the interaction relationship between the objects to be analyzed included in the target community, determining the community interaction parameters corresponding to the objects to be analyzed in the target community, wherein the community interaction parameters corresponding to the target objects to be analyzed are used to characterize the degree of interaction between the target objects to be analyzed and other objects to be analyzed included in the target community;

[0011] Determining a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community, wherein the community parameter is used to characterize the community scale of the target community;

[0012] Based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters corresponding to the target object to be analyzed are determined, the interaction parameters are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed, the degree of interaction characterized by the interaction parameters is positively correlated with the community size characterized by the community parameters, and is positively correlated with the degree of interaction characterized by the community interaction parameters corresponding to the target object to be analyzed.

[0013] In a second aspect, an embodiment of the present application discloses a data processing device, the device comprising an acquisition unit, an aggregation unit, a first determination unit, a second determination unit, and a third determination unit:

[0014] The acquisition unit is used to acquire a set of objects to be analyzed, wherein the set to be analyzed includes a plurality of objects to be analyzed;

[0015] The aggregation unit is used to aggregate the multiple objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed, and generate multiple object communities corresponding to the set of objects to be analyzed, wherein the interaction closeness between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the interaction closeness between the objects to be analyzed outside the target community and the target objects to be analyzed, the target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community;

[0016] The first determining unit is used to determine the community interaction parameters corresponding to the objects to be analyzed in the target community according to the interaction relationship between the objects to be analyzed included in the target community, and the community interaction parameters corresponding to the target objects to be analyzed are used to characterize the interaction closeness between the target objects to be analyzed and other objects to be analyzed included in the target community;

[0017] The second determining unit is used to determine a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community, wherein the community parameter is used to characterize the community scale of the target community;

[0018] The third determination unit is used to determine the interaction parameters corresponding to the target object to be analyzed based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed, the degree of interaction characterized by the interaction parameters is positively correlated with the community size characterized by the community parameters, and is positively correlated with the degree of interaction characterized by the community interaction parameters corresponding to the target object to be analyzed.

[0019] In a possible implementation manner, the first determining unit is specifically configured to:

[0020] For the target object to be analyzed, according to the interaction relationship between the objects to be analyzed included in the target community, sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions are determined, and the target sub-interaction parameters corresponding to the target object to be analyzed in the target dimension are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the target community under the target dimension, and the target dimension is any one of the multiple dimensions;

[0021] According to the sub-interaction parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed are determined.

[0022] In a possible implementation manner, the apparatus further includes a fourth determining unit:

[0023] The fourth determination unit is used to determine the parameter weight corresponding to the target dimension according to the parameter distribution difference of the sub-interaction parameter of the target dimension in the objects to be analyzed included in the target community, the parameter weight corresponding to the target dimension is used to identify the representation strength of the sub-interaction parameter of the target dimension on the interaction closeness between the target object to be analyzed and other objects to be analyzed included in the target community, and the parameter weight corresponding to the target dimension is positively correlated with the parameter distribution difference;

[0024] The first determining unit is specifically configured to:

[0025] The community interaction parameters corresponding to the target object to be analyzed are determined according to the sub-interaction parameters corresponding to the multiple dimensions of the target object to be analyzed and the parameter weights corresponding to the multiple dimensions.

[0026] In a possible implementation manner, the first determining unit is specifically configured to:

[0027] Determine the proximity parameter of the target object to be analyzed corresponding to the target dimension according to the proportional relationship between the target sub-interaction parameter and the maximum value of the sub-interaction parameter corresponding to the object to be analyzed included in the target community in the target dimension, wherein the proximity parameter is used to identify the proximity between the target sub-interaction parameter and the maximum value, and the maximum value is used to characterize the interaction closeness boundary of the object to be analyzed included in the target community in the target dimension;

[0028] According to the proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, a social interaction parameter corresponding to the target object to be analyzed is determined.

[0029] In a possible implementation, the maximum value includes an optimal value and a worst value, the proximity parameter includes an optimal proximity parameter and a worst proximity parameter, the optimal value is a sub-interaction parameter corresponding to the object to be analyzed with the largest interaction closeness under the target dimension among the objects to be analyzed included in the target community, and the worst value is a sub-interaction parameter corresponding to the object to be analyzed with the smallest interaction closeness under the target dimension among the objects to be analyzed included in the target community, and the first determining unit is specifically used to:

[0030] Determine the optimal proximity degree parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the optimal value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community, and determine the worst proximity degree parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the worst value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community;

[0031] According to the optimal proximity parameters and the worst proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed are determined, and the interaction closeness represented by the community interaction parameters corresponding to the target object to be analyzed is positively correlated with the proximity indicated by the optimal proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, and negatively correlated with the proximity indicated by the worst proximity parameters corresponding to the target object to be analyzed in the multiple dimensions.

[0032] In a possible implementation, the polymerization unit is specifically used for:

[0033] According to the interaction relationship between the multiple objects to be analyzed, an interaction topology corresponding to the set of objects to be analyzed is constructed, wherein the interaction topology includes object nodes corresponding to the multiple objects to be analyzed respectively, and connection identifiers between the object nodes, and the connection identifier is used to identify that there is an interaction behavior between the objects to be analyzed corresponding to any two object nodes connected by the connection identifier;

[0034] The multiple objects to be analyzed are aggregated based on the interaction topology to generate multiple object communities corresponding to the set of objects to be analyzed.

[0035] In a possible implementation, the object aggregation includes N aggregations, and the aggregation unit is specifically used to:

[0036] In the (i+1)th aggregation, any object to be analyzed that does not have a corresponding object community after the previous i aggregations is used as the object to be analyzed corresponding to the (i+1)th aggregation, and an associated object corresponding to the object to be analyzed corresponding to the (i+1)th aggregation is determined, where the associated object is an object having a connection identifier between corresponding object nodes in the interaction topology with the object to be analyzed corresponding to the (i+1)th aggregation;

[0037] Determine the module increase degrees respectively corresponding to the associated objects, wherein the module increase degree corresponding to the target associated object is used to identify the increase value of the modularity corresponding to the interaction topology after the object to be analyzed corresponding to the i+1th aggregation and the target associated object are aggregated into the same object community, the modularity is used to reflect the degree of excess of the first interaction closeness over the second interaction closeness in the interaction topology, the first interaction closeness is the interaction closeness between multiple objects to be analyzed in the same object community, the second interaction closeness is the interaction closeness between multiple objects to be analyzed in different object communities, the modularity is positively correlated with the excess degree, and the target associated object is any one of the associated objects;

[0038] In response to the module increase degree corresponding to the target-associated object being the largest and being a positive value, the object to be analyzed corresponding to the i+1th aggregation and the target-associated object are aggregated into the same object community to complete the i+1th aggregation, where N is a positive integer greater than 1 and i is a positive integer less than N.

[0039] In a possible implementation, the modularity corresponding to the interaction topology is determined in the following manner:

[0040] Determine the connection weights corresponding to the connection identifiers in the interaction topology, wherein the connection weights are used to characterize the interaction closeness between the objects to be analyzed corresponding to the two object nodes connected by the corresponding connection identifiers;

[0041] Determine, in the interactive topology, a first total connection weight corresponding to connection identifiers between object nodes included in the community to be analyzed, and a second total connection weight corresponding to connection identifiers between object nodes included in the community to be analyzed and object nodes not included in the community to be analyzed, wherein the community to be analyzed is any object community in the interactive topology;

[0042] Determining a submodule degree corresponding to the community to be analyzed according to the degree to which the first total connection weight exceeds the second total connection weight;

[0043] The modularity corresponding to the interactive topology is determined according to the sum of the sub-modularity corresponding to the communities to be analyzed in the interactive topology.

[0044] In a possible implementation manner, the apparatus further includes a fifth determining unit:

[0045] The fifth determining unit is used to determine the overall interaction parameter corresponding to the target community based on the community interaction parameters corresponding to the objects to be analyzed included in the target community, wherein the overall interaction parameter is used to characterize the overall interaction closeness of the objects to be analyzed included in the target community;

[0046] The third determining unit is specifically configured to:

[0047] Based on the overall interaction parameter, the community parameter and the community interaction parameter corresponding to the target object to be analyzed, the interaction parameter corresponding to the target object to be analyzed is determined, and the interaction closeness represented by the interaction parameter is positively correlated with the interaction closeness represented by the overall interaction parameter.

[0048] In a possible implementation manner, the apparatus further includes a sixth determining unit and a seventh determining unit:

[0049] The sixth determining unit is used to determine, according to the interaction parameters respectively corresponding to the multiple objects to be analyzed, M objects to be interacted with that have the greatest degree of interaction among the multiple objects to be analyzed, except the target object to be analyzed;

[0050] The seventh determination unit is used to determine the M objects to be interacted with as a set of objects to be interacted with corresponding to the target object to be analyzed, the target object to be analyzed has the authority to initiate a first target interaction to the M objects to be interacted with in a target application, M is a positive integer, and the multiple objects to be analyzed are objects using the target application.

[0051] In a possible implementation, the device further includes a first display unit and a first response unit:

[0052] The first display unit is used to display the object interface corresponding to the set of objects to be interacted in the target application through the terminal device corresponding to the target object to be analyzed, and the object interface includes object controls corresponding to the M objects to be interacted respectively;

[0053] The first response unit is used to initiate the first target interaction in the target application through the target object to be analyzed and the target object to be interacted with in response to obtaining a trigger operation for the object control corresponding to the target object to be interacted with, and the target object to be interacted with is any one of the M objects to be interacted with.

[0054] In a possible implementation manner, the device further includes an eighth determining unit, a permission unit, and a second responding unit:

[0055] The eighth determination unit is used to determine P objects to be analyzed having the greatest interaction closeness among the multiple objects to be analyzed according to the interaction parameters respectively corresponding to the multiple objects to be analyzed;

[0056] The permission unit is used to grant target permissions to the P objects to be analyzed through a target application;

[0057] The second response unit is used to grant the target object the target permission in response to the target object and the object with the target permission completing the second target interaction corresponding to the target permission in the target application, and the target object and the multiple objects to be analyzed are all objects using the target application.

[0058] In a possible implementation manner, the device further includes a third response unit and a fourth response unit:

[0059] The third response unit is configured to display a permission object interface to the target object through a terminal device corresponding to the target object in response to obtaining a request operation of the target object for the target permission, wherein the permission object interface includes object controls corresponding to the P objects to be analyzed respectively;

[0060] The fourth response unit is used to initiate the second target interaction in the target application through the target object and the object to be analyzed corresponding to the target object control in response to obtaining a trigger operation for the target object control, and the target object control is any one of the object controls corresponding to the P objects to be analyzed.

[0061] In a third aspect, an embodiment of the present application discloses a computer device, wherein the computer device includes a processor and a memory:

[0062] The memory is used to store a computer program and transmit the computer program to the processor;

[0063] The processor is used to execute the data processing method according to any one of the instructions in the computer program;

[0064] In a fourth aspect, an embodiment of the present application discloses a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the data processing method described in any one of the first aspects;

[0065] In a fifth aspect, an embodiment of the present application discloses a computer program product including a computer program, which, when executed on a computer device, enables the computer device to execute the data processing method described in any one of the first aspects.

[0066] It can be seen from the above technical scheme that when analyzing the interaction closeness of the object to be analyzed, the present application will first aggregate the multiple objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed, and aggregate the objects to be analyzed with close interaction into the same object community. For the target community, the community interaction parameters corresponding to any one of the target objects to be analyzed can be analyzed through the interaction relationship between the objects to be analyzed included in the target community. The community interaction parameters can reflect the interaction closeness of the target object to be analyzed among the objects to be analyzed included in the target community. Since when objects interact, they usually interact in the object community composed of objects with which they have a close interaction relationship, the community interaction parameters can more accurately reflect the interaction closeness of the actual interaction of the object to be analyzed. At the same time, for each object to be analyzed, the community interaction parameter analysis is performed in the object community composed of multiple objects to be analyzed with which they interact closely. Therefore, the community interaction parameter analysis corresponding to multiple objects to be analyzed is relatively equal, and a more accurate community interaction parameter analysis result can be obtained. In addition, for multiple objects to be analyzed as a whole, the degree of interaction between the target object to be analyzed and other objects to be analyzed in the set of objects to be analyzed also depends on the community size of the target community to which the target object to be analyzed belongs. The larger the community size of the target community, the more objects to be analyzed are affected by the target object to be analyzed during the interaction, and the greater the degree of interaction among the multiple objects to be analyzed as a whole. Based on this, the interaction parameters corresponding to the target object to be analyzed can be determined based on the community parameters that can identify the community size of the target community and the community interaction parameters corresponding to the target object to be analyzed. The interaction parameters can truly and accurately reflect the degree of interaction of the target object to be analyzed among the multiple objects to be analyzed as a whole, which helps to more accurately analyze the interactive impact of the target object to be analyzed on the multiple objects to be analyzed as a whole, so that related operations based on the degree of interaction can be more reasonably performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 A schematic diagram of a data processing method in an actual application scenario provided by an embodiment of the present application;

[0069] Figure 2 A flowchart of a data processing method provided in an embodiment of the present application;

[0070] Figure 3 A schematic diagram of a data processing method provided in an embodiment of the present application;

[0071] Figure 4 A schematic diagram of a data processing method provided in an embodiment of the present application;

[0072] Figure 5 A schematic diagram of a data processing method provided in an embodiment of the present application;

[0073] Figure 6 A schematic diagram of a data processing method provided in an embodiment of the present application;

[0074] Figure 7 A schematic diagram of a data processing method provided in an embodiment of the present application;

[0075] Figure 8 A flowchart of a data processing method in an actual application scenario provided by an embodiment of the present application;

[0076] Fig. 9 A structural block diagram of a data processing device provided in an embodiment of the present application;

[0077] Fig.10 A structural diagram of a terminal provided in an embodiment of the present application;

[0078] Fig.11 A structural diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0079] The embodiments of the present application are described below in conjunction with the accompanying drawings.

[0080] In related technologies, when analyzing the degree of interaction of an object, multiple objects associated with the object are directly obtained. Based on the analysis of the interaction relationship between the object and the multiple objects, the interaction parameters of the degree of interaction between the object and other objects in the object community constituted by the multiple objects can be reflected, and the interaction parameters are directly used as indicators for judging the degree of interaction corresponding to the object for subsequent applications.

[0081] However, in the related art, when obtaining the object community for analysis, the interaction closeness between multiple objects in the object community is not considered first, so that when performing interaction parameter analysis on different objects, the interaction closeness between multiple objects in the object community for analysis and the object to be analyzed may be different, thereby affecting the interaction parameter analysis results of different objects. For example, for object A, multiple objects with close interaction with it may be randomly selected for analysis, so that the interaction closeness obtained is high; for object B, multiple objects with less close interaction with it may be randomly selected for analysis, so that the interaction closeness obtained is low. At the same time, when analyzing the interaction closeness, the related art does not consider the impact of the community size of the object community on the interaction closeness. For example, although the interaction closeness of object A in a certain object community is high, the number of objects included in the object community is small. Therefore, from the perspective of the overall object group including the object community, the interaction closeness of object A in the overall object group may be low, so the interaction closeness of object A in the object community is determined as the interaction closeness of object A in the overall object group. It is inaccurate. It can be seen that the parameter analysis method in the related art is difficult to obtain accurate interaction parameters.

[0082] In order to solve the above technical problems, the present application provides a data processing method. On the one hand, during analysis, multiple objects to be analyzed will be aggregated based on the interaction relationship, and the objects to be analyzed with relatively close interaction relationships will be aggregated into the same object community, so that when analyzing the interaction closeness between multiple objects to be analyzed and other objects to be analyzed in the object community to which they belong, a relatively equal and balanced community interaction parameter can be obtained. On the other hand, the computer device can further determine the interaction parameter based on the community interaction parameter in combination with the community size of the community to which the object to be analyzed belongs, so that the interaction parameter can integrate the influence of the community size on the interaction closeness, and accurately characterize the interaction closeness between the object to be analyzed and other objects to be analyzed among multiple objects to be analyzed.

[0083] It is understandable that the method can be applied to a computer device, which is a computer device capable of data processing, such as a terminal device or a server. The method can be executed independently by a terminal device or a server, or can be applied to a network scenario in which a terminal device and a server communicate, and is executed by the cooperation of the terminal device and the server. Among them, the terminal device can be a mobile phone, a tablet computer, a laptop computer, a desktop computer and other devices. The server can be understood as an application server or a Web server. In actual deployment, the server can be an independent server, a cluster server, or a cloud server, etc.

[0084] In order to facilitate understanding of the technical solution provided by the present application, a data processing method provided by an embodiment of the present application will be introduced below in combination with an actual application scenario.

[0085] See also Figure 1 , Figure 1 A schematic diagram of a data processing method in an actual application scenario provided in an embodiment of the present application. In this actual application scenario, the computer device may be a data processing server 101 with data processing capabilities.

[0086] The data processing server 101 may obtain a set of objects to be analyzed including multiple objects to be analyzed including objects A to H, and then aggregate the objects to be analyzed that interact closely with each other into the same object community based on the interaction relationship. Figure 1 As shown in the figure, objects A, B, and E interact with each other, and objects A and E do not interact with other objects to be analyzed except object B. Therefore, objects A, E, and B can be judged as objects with relatively close interactions, and they are aggregated into object community A. Similarly, objects C, D, H, F, and G are clustered into object community B.

[0087] Taking object C as an example, the data processing server 101 can determine the community interaction parameter corresponding to object C based on the interaction relationship between multiple objects to be analyzed in object community B. The community interaction parameter can characterize the degree of interaction between object C and other objects to be analyzed in object community B. The data processing server 101 can determine the community parameter corresponding to object community B based on the community scale of object community B. The community parameter is used to characterize the community scale of object community B. Finally, the data processing server 101 can determine the interaction parameter actually corresponding to object C in combination with the community interaction parameter and the community parameter. The interaction parameter is used to characterize the degree of interaction between object C and other objects to be analyzed included in the set of objects to be analyzed. For example, since object community A includes 3 objects to be analyzed and object community B includes 5 objects to be analyzed, it can be determined that the community parameter corresponding to object community A is 0.3 and the community parameter corresponding to object community B is 0.5. Assuming that the community interaction parameters are positively correlated with the degree of interaction, and the community interaction parameters of objects C and A are the same, since the community parameters of object community B are greater than those of object community A, the interaction parameters of object C that are finally determined are greater than those of object A, which fits the actual situation that object C has more closely interacted objects and a higher degree of interaction among multiple objects to be analyzed.

[0088] It can be seen that, since the embodiment of the present application performs community interaction parameter analysis for each object to be analyzed in the object community to which it belongs, and the interactions between the objects to be analyzed in the same object community are relatively close, it is possible to measure the community interaction parameters under similar standards and accurately analyze the community interaction parameters. In addition, determining the interaction parameters in combination with the community scale can reasonably reflect the degree of interaction closeness of the object to be analyzed in the overall group of multiple objects to be analyzed, thereby improving the rationality and accuracy of the interaction closeness analysis.

[0089] Next, a data processing method provided in an embodiment of the present application will be introduced in conjunction with the accompanying drawings.

[0090] See also Figure 2 , Figure 2 A flowchart of a data processing method provided in an embodiment of the present application. In this embodiment, the computer device may be any of the above-mentioned computer devices with data processing capabilities. The method includes:

[0091] S201: Obtain a set of objects to be analyzed.

[0092] The set to be analyzed includes multiple objects to be analyzed, and the objects to be analyzed may be any objects that can generate interactions, such as users who use applications, users who participate in certain activities, etc. For example, when it is necessary to analyze the interaction parameters of users who use a specific application, the computer device may randomly select multiple users from the user group of the application as multiple objects to be analyzed.

[0093] S202: Based on the interaction relationship between the multiple objects to be analyzed, the multiple objects to be analyzed are aggregated to generate multiple object communities corresponding to the set of objects to be analyzed.

[0094] The interaction relationship is used to identify whether the objects to be analyzed have interactive behaviors or specific relationships, etc. For example, in a game application, the interaction relationship may be that the objects to be analyzed have completed a game together, or that the objects to be analyzed have a friend relationship, etc. The interaction relationship can be used to characterize the interaction between multiple objects to be analyzed, and thus the degree of interaction between the objects to be analyzed can be analyzed based on the interaction relationship.

[0095] In order to analyze the interaction closeness of each object to be analyzed under the same measurement standard, the computer device first needs to determine the object community used to analyze each object to be analyzed. It can be understood that since in actual interaction, the objects to be analyzed interact with other objects that interact closely with themselves, the object community composed of the objects to be analyzed that interact closely with the objects to be analyzed can more realistically reflect the interaction situation of the objects to be analyzed in actual interaction.

[0096] Based on this, the computer device can aggregate multiple objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed, so as to aggregate the objects to be analyzed that interact relatively closely with each other into the same object community. The object community refers to a community composed of objects to be analyzed. Taking the target community as an example, the degree of interaction between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the degree of interaction between the objects to be analyzed outside the target community and the target objects to be analyzed. The target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community. It can be seen that for each object community, the degree of interaction between the objects to be analyzed within the community is higher than the degree of interaction between the objects to be analyzed within the community and the objects to be analyzed outside the community, so that the interaction group corresponding to each object to be analyzed can be simulated under actual interaction conditions.

[0097] For example, in a game application, a user usually interacts with his or her game friends or other users with whom he or she often plays games. Therefore, in a game application, analyzing the degree of interaction with users with whom the user interacts closely is more in line with the user's actual interaction situation when using the game application.

[0098] S203: Determine community interaction parameters corresponding to the objects to be analyzed in the target community according to the interaction relationships between the objects to be analyzed included in the target community.

[0099] Taking the target community as an example, the computer device can analyze the interaction closeness of each object to be analyzed in the target community among the objects to be analyzed included in the target community according to the interaction relationship between the objects to be analyzed included in the target community, thereby obtaining the community interaction parameters corresponding to each object to be analyzed, wherein the community interaction parameters corresponding to the target object to be analyzed are used to characterize the interaction closeness between the target object to be analyzed and other objects to be analyzed included in the target community. Since in this application, the community interaction parameters of each object to be analyzed are analyzed based on the objects to be analyzed included in the object community in which each object to be analyzed is located, and the objects to be analyzed in each community are objects that interact more closely with each other than objects outside the community. Therefore, for each object to be analyzed, when analyzing the community interaction parameters, the analysis is based on the objects to be analyzed that are relatively closely interacting with the object to be analyzed and have a higher probability of interacting under normal interaction conditions. For multiple objects to be analyzed, the interaction relationship based on them is relatively equal and balanced, so the determined community interaction parameters can reduce the interference of differences in other factors.

[0100] It can be seen that the analysis of community interaction parameters in this application has high accuracy and rationality, and is more in line with the actual interaction situation of the object to be analyzed.

[0101] S204: Determine a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community.

[0102] Among them, the community parameter is used to characterize the community size of the target community. It is understandable that the ultimate goal of this application is to determine the interaction of the object to be analyzed among the multiple objects to be analyzed included in the set of objects to be analyzed, and the community size is also a key factor affecting the closeness of the interaction of the object to be analyzed. Generally, the more objects that interact closely with the object to be analyzed, the higher the closeness of the interaction of the object to be analyzed in the set of objects to be analyzed.

[0103] If the degree of interaction of the object to be analyzed among multiple objects to be analyzed is analyzed based only on community interaction parameters, it may happen that a certain object to be analyzed is in a small object community and interacts closely with the objects to be analyzed in the community, so it is determined that the interaction of the object to be analyzed among multiple objects to be analyzed is also close. In fact, the object community where the object to be analyzed is located only includes a very small number of objects to be analyzed among multiple objects to be analyzed. Therefore, from the perspective of the multiple objects to be analyzed as a whole, the actual degree of interaction of the object to be analyzed is relatively small. Alternatively, the community interaction parameter of a certain object to be analyzed is not high, but the object community in which it is located includes a large number of objects to be analyzed. Therefore, from the perspective of the objects to be analyzed as a whole, the object to be analyzed also has a certain degree of interaction closeness.

[0104] Based on this, in order to analyze the interaction closeness more accurately and reasonably, when determining the interaction closeness of the target object to be analyzed among multiple objects to be analyzed, the computer device can first determine the community parameters corresponding to the target community according to the number of objects to be analyzed included in the target community.

[0105] S205: Determine the interaction parameters corresponding to the target object to be analyzed based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed.

[0106] Among them, the interaction parameter is used to characterize the interaction closeness between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed. The interaction closeness represented by the interaction parameter is positively correlated with the community size represented by the community parameter, and is positively correlated with the interaction closeness represented by the community interaction parameter corresponding to the target object to be analyzed. That is, the greater the interaction closeness of the target object to be analyzed in the target community, and the larger the community size of the target community, the higher the interaction closeness between the determined target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed. In actual situations, if the target object to be analyzed has a closer interaction relationship with more objects to be analyzed, then from the perspective of the overall object to be analyzed dimension, the interactive behavior of the target object to be analyzed has a wider range of influence and a greater impact, and therefore corresponds to a higher degree of interaction closeness. It can be seen that the analysis method of the present application has a high rationality.

[0107] It can be seen from the above technical solution that when analyzing the interaction closeness of the object to be analyzed, on the one hand, the community interaction parameters can more accurately reflect the interaction closeness of the object to be analyzed during actual interaction. On the other hand, for each object to be analyzed, the community interaction parameter analysis is performed in the object community composed of multiple objects to be analyzed that interact more closely with it. Therefore, the community interaction parameter analysis corresponding to multiple objects to be analyzed is relatively equal, and a more accurate community interaction parameter analysis result can be obtained. In addition, for multiple objects to be analyzed as a whole, the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed also depends on the community size of the target community to which the target object to be analyzed belongs. The larger the community size of the target community, the more objects to be analyzed are affected by the target object to be analyzed during the interaction, and the greater the degree of interaction among the multiple objects to be analyzed as a whole. Based on this, the interaction parameters corresponding to the target object to be analyzed can be determined based on the community parameters that can identify the community size of the target community and the community interaction parameters corresponding to the target object to be analyzed. The interaction parameters can truly and accurately reflect the degree of interaction of the target object to be analyzed among the multiple objects to be analyzed as a whole, which helps to more accurately analyze the interactive impact of the target object to be analyzed on the multiple objects to be analyzed as a whole, so that related operations based on the degree of interaction can be more reasonably performed.

[0108] Next, based on the above embodiments, the technical branches and technical details involved in the embodiments will be described in detail.

[0109] First, when analyzing the social interaction parameters, the computer device can use multiple parameter analysis methods. In order to improve the accuracy of parameter analysis as much as possible, in a possible implementation method, the computer device can combine parameters of multiple dimensions to analyze the closeness of interaction.

[0110] When executing step S203, the computer device may execute steps S2031-S2032 (not shown in the figure), where steps S2031-S2032 are a possible implementation of step S203, including:

[0111] S2031: For the target object to be analyzed, according to the interaction relationship between the objects to be analyzed included in the target community, determine the sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions.

[0112] In an embodiment of the present application, a computer device can analyze the degree of interaction between a target object to be analyzed and other objects to be analyzed included in a target community from multiple dimensions based on the interaction relationship between the objects to be analyzed included in the target community, and obtain sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions, where the sub-interaction parameters are used to characterize the degree of interaction between the object to be analyzed and other objects to be analyzed included in the object community in a certain dimension. For example, a target sub-interaction parameter corresponding to the target object to be analyzed in a target dimension is used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the target community in the target dimension, where the target dimension is any one of the multiple dimensions.

[0113] Among them, multiple dimensions may be, for example, five node centrality algorithm dimensions, namely, degree dimension, H index dimension, W index dimension, k-core index dimension, and collaborative influence dimension. The parameters obtained by each node centrality algorithm can reflect the degree of interaction of the object to be analyzed to a certain extent.

[0114] S2032: Determine the community interaction parameters corresponding to the target object to be analyzed according to the sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions.

[0115] By combining the interaction closeness of the target object to be analyzed under multiple analysis dimensions, the interaction closeness of the target object to be analyzed among the objects to be analyzed included in the target community can be analyzed more accurately, thereby determining more accurate community interaction parameters.

[0116] It is understandable that the parameters of different dimensions may have different representation strengths for the interaction closeness of the objects to be analyzed. For example, if the sub-interaction parameters of different objects to be analyzed in a certain dimension are relatively close and the difference is small, it means that the sub-interaction parameters of this dimension have a small representation strength for the difference in the interaction closeness of different objects to be analyzed. Therefore, when performing community interaction parameter analysis based on the sub-interaction parameters of this dimension, it may be difficult to reflect the difference in the interaction closeness of different objects to be analyzed in the same community.

[0117] Based on this, in a possible implementation method, in order to make the community interaction parameters more significantly reflect the degree of interaction of each object to be analyzed among the objects to be analyzed included in the object community, the computer device can assign different parameter weights based on the differences in sub-interaction parameters of different dimensions between different objects to be analyzed.

[0118] Taking the target dimension as an example, the computer device can determine the parameter weight corresponding to the target dimension according to the parameter distribution difference of the sub-interaction parameter of the target dimension in the objects to be analyzed included in the target community. The parameter weight corresponding to the target dimension is used to identify the representation strength of the sub-interaction parameter of the target dimension on the interaction closeness between the target object to be analyzed and other objects to be analyzed included in the target community. The parameter weight corresponding to the target dimension is positively correlated with the parameter distribution difference. That is, the greater the difference in the parameter distribution of the sub-interaction parameter of the target dimension in the objects to be analyzed included in the target community, it means that when the interaction closeness of different objects to be analyzed in the objects to be analyzed included in the target community is different, the corresponding sub-interaction parameter difference under the target dimension is greater, so the corresponding sub-interaction parameter under the target dimension has a more significant representation effect on the interaction closeness of the objects to be analyzed, so a higher parameter weight can be assigned.

[0119] When executing step S2032, the computer device may execute step S20321 (not shown in the figure), where step S20321 is a possible implementation of step S2032, including:

[0120] S20321: Determine the community interaction parameters corresponding to the target object to be analyzed according to the sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions and the parameter weights corresponding to the multiple dimensions.

[0121] Among them, the greater the parameter weight corresponding to the dimension, the greater the representation of the interaction closeness of the object to be analyzed among the objects to be analyzed included in the target community by the sub-interaction parameter under the dimension, that is, the greater the impact on the community interaction parameter. Conversely, the smaller the parameter weight corresponding to the dimension, the smaller the impact on the community interaction parameter. The community interaction parameters determined in this way can highlight the differences in the interaction closeness between different objects to be analyzed, so that the community interaction parameters can more accurately represent the interaction closeness corresponding to each object to be analyzed in the target community.

[0122] For example, in a possible implementation, for each object i to be analyzed (1≤i≤N), the sub-interaction parameters of M dimensions can be analyzed to obtain a sub-interaction parameter matrix V={v ij}, i = 1, ..., N, j = 1, ..., M, where v ij Represents the sub-interaction parameter corresponding to the object i to be analyzed in the jth dimension. After calculating V, in order to reduce the interference of different dimensions of sub-interaction parameters in different dimensions on the analysis of interaction closeness, the computer device can normalize the feature matrix V to obtain R = {r ij}, to ensure that the dimensions of the sub-interaction parameters in each dimension are consistent, that is:

[0123]

[0124] Among them, r ij Represents the normalized parameters of the sub-interaction parameters corresponding to the object i to be analyzed in the jth dimension. The computer device can judge the difference in the parameter distribution of the sub-interaction parameters in a certain dimension based on the entropy value of the parameter distribution of the sub-interaction parameters. The smaller the entropy value, the greater the difference in the parameter distribution of the sub-interaction parameters, and the greater the influence of the dimension on the sub-interaction parameters (i.e., weight). If the values ​​of the sub-interaction parameters in a certain dimension are all equal in multiple objects to be analyzed, then the dimension plays a smaller role in the interaction closeness analysis. Therefore, define w j is the weight of dimension j, and the calculation formula is as follows:

[0125] wj=1-ej

[0126] where e j is the information entropy of dimension j, mathematically expressed as:

[0127]

[0128] in It represents the proportion of the sub-interaction parameter corresponding to the object to be analyzed i under dimension j in the sum of the sub-interaction parameters corresponding to the N objects to be analyzed under dimension j. ln represents the logarithmic operation with base e.

[0129] Finally, the computer device can obtain the sub-interaction parameter matrix X after weighted processing of the sub-interaction parameters:

[0130] X={x ij},where x ij =w j *r ij

[0131] As mentioned above, the present application can combine sub-interaction parameters of multiple dimensions to comprehensively analyze the interaction closeness of the object to be analyzed, wherein the methods of analyzing the community interaction parameters based on the sub-interaction parameters can also include multiple methods. In one possible implementation, the computer device can analyze the community interaction parameters corresponding to the target object to be analyzed based on the difference between the sub-interaction parameters corresponding to the target object to be analyzed and the maximum values ​​of the sub-interaction parameters under each dimension.

[0132] When executing step S2032, the computer device may execute steps S20322-S20323 (not shown in the figure), where steps S20322-S20323 are a possible implementation of step S2032, including:

[0133] S20322: Determine a proximity parameter corresponding to the target object to be analyzed in the target dimension according to a proportional relationship between the target sub-interaction parameter and the maximum value of the sub-interaction parameter corresponding to the object to be analyzed included in the target community in the target dimension.

[0134] The maximum value is used to characterize the interaction closeness boundary of the analyzed objects included in the target community under the target dimension, that is, the sub-interaction parameter corresponding to the analyzed object with the largest or smallest interaction closeness under the target dimension. The proximity parameter is used to identify the proximity between the target sub-interaction parameter and the maximum value.

[0135] This application has the following two advantages in determining the degree of proximity based on a proportional relationship:

[0136] On the one hand, the present application does not only measure the interaction of the object to be analyzed among multiple objects to be analyzed based on the size of the sub-interaction parameter itself, but first analyzes the maximum values ​​of the sub-interaction parameters corresponding to multiple objects to be analyzed in multiple dimensions. The maximum value is used to characterize the interaction closeness boundary of multiple objects to be analyzed under the target dimension, that is, the maximum value is the sub-interaction parameter corresponding to the object to be analyzed with the maximum or minimum interaction closeness under each dimension. Therefore, by comparing the maximum value with the sub-interaction parameter corresponding to the target object to be analyzed in the target dimension, the interaction closeness level of the target object to be analyzed among multiple objects to be analyzed can be more intuitively characterized.

[0137] The interaction closeness level here is different from the interaction closeness represented by the sub-interaction parameter. The sub-interaction parameter represents the interaction closeness of the parameter itself in terms of the numerical value, which is the interaction closeness corresponding to the object to be analyzed among multiple objects to be analyzed, and is not analyzed in combination with other objects to be analyzed in the set of objects to be analyzed; while the interaction closeness level obtained by comparison can represent the interaction closeness level of the object to be analyzed compared with the overall interaction closeness of multiple objects to be analyzed. For example, if we only look at the sub-interaction parameter, in dimension 1, if the sub-interaction parameter is positively correlated with the interaction closeness, the interaction closeness corresponding to the object to be analyzed with a sub-interaction parameter of 10 is greater than the interaction closeness corresponding to the object to be analyzed with a sub-interaction parameter of 6; in dimension 2, if the sub-interaction parameter is positively correlated with the interaction closeness, the interaction closeness corresponding to the object to be analyzed with a sub-interaction parameter of 50 is greater than the interaction closeness corresponding to the object to be analyzed with a sub-interaction parameter of 30. If the maximum value of the interaction closeness corresponding to dimension 1 is 10, and the maximum value of the interaction closeness corresponding to dimension 2 is 100, then the object to be analyzed with a corresponding sub-interaction parameter of 6 in dimension 1 is actually closer to the maximum value than the object to be analyzed with a corresponding sub-interaction parameter of 50 in dimension 2. Therefore, relative to the set of objects to be analyzed, there is actually a higher level of interaction closeness in the dimension. It can be seen that by measuring the interaction closeness by comparing with the maximum value, the interaction closeness of multiple objects to be analyzed can be analyzed comprehensively, so that the obtained interaction closeness is more in line with the actual interaction situation of the object to be analyzed in the set of objects to be analyzed.

[0138] On the other hand, the above mentioned benefits of using the maximum value and sub-interaction parameters of the present application to measure the closeness of interaction. It is understandable that the parameter dimensions in different dimensions may be different, that is, the parameter ranges may be different. For example, the parameter dimension in a certain dimension may be <0-10>, and the parameter dimension in another dimension may be <0-100>. When the dimensions are different, the same parameter difference in different dimensions represents different closeness of interaction. Therefore, in order to reasonably combine the actual interaction situation represented by the sub-interaction parameters and the maximum value in each dimension, the computer device needs to analyze the parameters of each dimension with the same dimension.

[0139] Therefore, in the present application, the difference between the sub-interaction parameter and the maximum value is not directly used, but the actual interaction of the object to be analyzed in the set of objects to be analyzed under each dimension is measured based on the proportional relationship between the sub-interaction parameter and the maximum value, so as to avoid the difference caused by different dimensions. Specifically, for the target object to be analyzed, the computer device can determine the proximity parameter corresponding to the target object to be analyzed in the target dimension based on the proportional relationship between the target sub-interaction parameter corresponding to the target object to be analyzed in the target dimension and the maximum value of the sub-interaction parameter corresponding to the target dimension of multiple objects to be analyzed. The proximity parameter is used to identify the degree of proximity between the target sub-interaction parameter and the maximum value.

[0140] For example, in dimension 1, if the maximum value is 100 and the sub-interaction parameter is 60, the proximity parameter is determined based on the proportional relationship of 0.6; in dimension 2, if the maximum value is 10 and the sub-interaction parameter is 6, the proximity parameter is also determined based on the proportional relationship of 0.6, so the effect of balanced analysis of parameters of different dimensions can be achieved.

[0141] S20323: Determine the community interaction parameters corresponding to the target object to be analyzed according to the proximity parameters corresponding to the target object to be analyzed in multiple dimensions.

[0142] It can be understood that since the sub-interaction parameter can characterize the interaction closeness of the object to be analyzed, by comparing the maximum value with the sub-interaction parameter corresponding to the target object to be analyzed in the target dimension, the closeness between the interaction closeness corresponding to the target object to be analyzed in the target dimension and the interaction closeness boundary can be reflected, so that the proximity parameter can more intuitively characterize the interaction closeness level of the target object to be analyzed among multiple objects to be analyzed, and then the accurate community interaction parameter can be determined based on the proximity parameter. For example, if the sub-interaction parameter of the target object to be analyzed in the target dimension is relatively close to the sub-interaction parameter corresponding to the object to be analyzed with the greatest interaction closeness in the target dimension, then the target object to be analyzed is likely to also have a greater interaction closeness in the target dimension.

[0143] Among them, when analyzing the proximity parameter based on the maximum value, the computer device can take the sub-interaction parameter corresponding to the object to be analyzed with the greatest interaction closeness as the optimal value, and perform the proximity parameter analysis based on the optimal value. The closer the sub-interaction parameter is to the optimal value, the greater the interaction closeness of the object to be analyzed is generally. The computer device can also take the sub-interaction parameter corresponding to the object to be analyzed with the least interaction closeness as the worst value, and perform the proximity parameter analysis based on the worst value. The closer the sub-interaction parameter is to the worst value, the smaller the interaction closeness of the object to be analyzed is generally.

[0144] In a multi-dimensional parameter scenario, since the present application involves sub-interaction parameters in multiple dimensions, each object to be analyzed can be regarded as a point in a high-dimensional space, wherein the maximum values ​​of multiple objects to be analyzed in each dimension can constitute an optimal value point and a worst value point, the optimal value point corresponds to the sub-interaction parameter corresponding to the object to be analyzed with the greatest interaction closeness in each dimension, and the worst value point corresponds to the sub-interaction parameter corresponding to the object to be analyzed with the least interaction closeness in each dimension. If the point corresponding to the target object to be analyzed is closer to the optimal value point, it means that the sub-interaction parameter is closer to the sub-interaction parameter corresponding to the maximum interaction closeness in multiple dimensions as a whole; if the point corresponding to the target object to be analyzed is closer to the worst value point, it means that the sub-interaction parameter is closer to the sub-interaction parameter corresponding to the least interaction closeness in multiple dimensions as a whole.

[0145] It is understandable that in high-dimensional space, being farther away from one point may not mean being closer to another point, see Figure 3 , compared with point 1, point 2 is farther from the optimal point, but also farther from the worst point. Based on this, in a possible implementation, in order to more accurately measure the interaction closeness of the target object to be analyzed among multiple objects to be analyzed, the computer device can combine the maximum values ​​of the two interaction closeness boundaries to determine the interaction parameter corresponding to the target object to be analyzed.

[0146] In this implementation, the maximum value includes an optimal value and a worst value, and the proximity parameter includes an optimal proximity parameter and a worst proximity parameter, wherein the optimal value is a sub-interaction parameter corresponding to the object to be analyzed with the largest interaction closeness under the target dimension among the objects to be analyzed included in the target community, and the worst value is a sub-interaction parameter corresponding to the object to be analyzed with the smallest interaction closeness under the target dimension among the objects to be analyzed included in the target community. When executing step S20322, the computer device may execute step S203221 (not shown in the figure), which is a possible implementation of step S20322, including:

[0147] S203221: Determine the optimal proximity parameter of the target object to be analyzed in the target dimension based on the proportional relationship between the target sub-interaction parameter and the optimal value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community, and determine the worst proximity parameter of the target object to be analyzed in the target dimension based on the proportional relationship between the target sub-interaction parameter and the worst value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community.

[0148] Among them, the greater the degree of proximity indicated by the optimal proximity parameter, the greater the interaction closeness of the target object to be analyzed under the target dimension to a certain extent; the greater the degree of proximity indicated by the worst proximity parameter, the smaller the interaction closeness of the target object to be analyzed under the target dimension to a certain extent.

[0149] When executing step S20323, the computer device may execute step S203231 (not shown in the figure), where step S203231 is a possible implementation of step S20323, including:

[0150] S203231: Determine the community interaction parameters corresponding to the target object to be analyzed according to the optimal proximity parameters and the worst proximity parameters corresponding to the target object to be analyzed in multiple dimensions.

[0151] Among them, the interaction closeness represented by the community interaction parameter corresponding to the target object to be analyzed is positively correlated with the closeness indicated by the optimal closeness parameter corresponding to the target object to be analyzed in multiple dimensions, and negatively correlated with the closeness indicated by the worst closeness parameter corresponding to the target object to be analyzed in multiple dimensions. Therefore, the computer device can combine the two interaction closeness boundaries of the optimal value and the worst value to more accurately analyze the interaction closeness of the target object to be analyzed, thereby improving the analysis accuracy in the multi-dimensional parameter scenario.

[0152] For example, after obtaining the sub-interaction parameter matrix X and the community division C through the above process, the computer device can define A k is the optimal value in the kth object community, and is a vector composed of the optimal values ​​of the sub-interaction parameters under each dimension:

[0153] A k =(α k1 ,…,α kM ),

[0154] in is the optimal value of the jth dimension in the kth object community. Similarly, the worst value B k :

[0155]

[0156] β kj is the worst value under the jth dimension in the kth object community.

[0157] Then, the computer device can calculate the relative entropy (also called KL divergence) between the optimal value and the worst value of the object to be analyzed i and its object community. The relative entropy can reflect the difference between the sub-interaction parameters of the object to be analyzed and the optimal value and the worst value in each dimension. The relative entropy between the optimal value and the worst value of the object to be analyzed i and its object community is expressed as express:

[0158]

[0159]

[0160] x ij is the sub-interaction parameter corresponding to the object i to be analyzed in the jth dimension. The computer device can define the community interaction parameter of the object i to be analyzed as The ratio of the sum of the two, that is The intuitive meaning of this value is: if the sub-interaction parameters of the object i under analysis are closer to the optimal value ( The smaller it is), the farther away from the worst value ( The larger the value is), the larger the community interaction parameter corresponding to the object to be analyzed is. At this time, the community interaction parameter is positively correlated with the identified interaction closeness.

[0161] Furthermore, suppose G i is the object community where the object i to be analyzed belongs, that is, G i ={j:c i =c j The computer device can define the interaction parameter f of the object to be analyzed. i Equal to the community interaction parameter and the number of objects to be analyzed included in the community |G i | (i.e. as a community parameter), that is:

[0162]

[0163] Next, the above object aggregation process will be introduced in detail.

[0164] In a possible implementation, the computer device may aggregate objects based on the form of interactive topology. When executing step S202, the computer device may execute steps S2021-S2022 (not shown in the figure), which are a possible implementation of step S202, including:

[0165] S2021: Construct an interaction topology corresponding to the set of objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed.

[0166] By analyzing the interaction relationship between multiple objects to be analyzed, the computer device can learn the interaction behavior between each object to be analyzed, thereby constructing the interaction topology. The interaction topology includes object nodes corresponding to multiple objects to be analyzed, and connection identifiers between object nodes. The connection identifier is used to identify that there is an interaction behavior between any two objects to be analyzed corresponding to the object nodes connected by the connection identifier. The interaction behavior can be any behavior involving object interaction.

[0167] Taking the game application scenario as an example, in the game application, the interactive behavior may be adding game friends, playing a game, etc. If two objects to be analyzed have a friend relationship in the game, or have played a game together, then there may be a connection identifier between the object nodes corresponding to the two objects to be analyzed in the interaction topology.

[0168] S2022: Aggregate the multiple objects to be analyzed based on the interactive topology to generate multiple object communities corresponding to the set of objects to be analyzed.

[0169] Since the connection identifiers can reflect the interactive behaviors between multiple objects to be analyzed, based on the distribution of connection identifiers between multiple object nodes, the computer device can analyze the objects to be analyzed that interact closely with each other and the objects that interact sparsely with each other, so that an object community composed of objects to be analyzed that interact closely with each other can be aggregated from the multiple objects to be analyzed. This object aggregation method can more intuitively and accurately analyze the degree of interaction between multiple objects to be analyzed, so that accurate object aggregation can be performed to obtain reasonable object community aggregation results.

[0170] For example, Figure 4 In the figure, object community A and object community B may be object communities divided based on interactive topology. The connection identifier in the interactive topology may be a line between object nodes. If there is a line between two object nodes, it means that there is interactive behavior between the objects to be analyzed corresponding to the two object nodes.

[0171] Specifically, when performing object aggregation, in order to improve the accuracy of object aggregation, the computer device may perform fine-grained object aggregation with a single object to be analyzed as the granularity.

[0172] In a possible implementation, object aggregation includes N aggregations. When executing step S2022, the computer device may execute steps S20221-S20223 (not shown in the figure). Steps S20221-S20223 are a possible implementation of step S2022, including:

[0173] S20221: In the (i+1)th aggregation, any object to be analyzed that has no corresponding object community after the previous i aggregations is used as the object to be analyzed corresponding to the (i+1)th aggregation, and the associated object corresponding to the object to be analyzed corresponding to the (i+1)th aggregation is determined.

[0174] Among them, the associated object is the object with a connection identifier between the object nodes corresponding to the object to be analyzed corresponding to the i+1th aggregation in the interactive topology, that is, the object to be analyzed that has interactive behavior with the object to be analyzed. The existence of interactive behavior between two objects to be analyzed is the basis for the close interactive relationship between the two objects to be analyzed. If there is no interactive behavior between the two objects to be analyzed at all, the degree of interaction between the two objects to be analyzed is usually low, so non-associated objects can be ignored in object aggregation. N is a positive integer greater than 1, and i is a positive integer less than N. That is, in each aggregation, it is performed on an object to be analyzed to determine the object community corresponding to the object to be analyzed.

[0175] S20222: Determine the module addition degrees corresponding to the associated objects.

[0176] Among them, the module increase degree corresponding to the target associated object is used to identify the increase value of the modularity corresponding to the interaction topology after the object to be analyzed corresponding to the i+1th aggregation and the target associated object are aggregated into the same object community. The modularity is used to reflect the degree of excess of the first interaction closeness over the second interaction closeness in the interaction topology. The first interaction closeness is the interaction closeness between multiple objects to be analyzed in the same object community, and the second interaction closeness is the interaction closeness between multiple objects to be analyzed in different object communities. The modularity is positively correlated with the excess degree.

[0177] That is, the greater the modularity, the closer the interaction between the objects to be analyzed in the same object community is than that between the objects to be analyzed in different object communities in the interaction topology, which means that the objects to be analyzed that interact closely with each other are more effectively aggregated into the same object community, and the objects to be analyzed that interact sparsely with each other are aggregated into different object communities, which means that the object aggregation effect is better. The target associated object can be any one of the associated objects.

[0178] S20223: In response to the module increase degree corresponding to the target-related object being the largest and being a positive value, the object to be analyzed corresponding to the (i+1)th aggregation and the target-related object are aggregated into the same object community, thereby completing the (i+1)th aggregation.

[0179] As mentioned above, the module increase degree can reflect the aggregation effect after aggregating the object to be analyzed of the i+1th aggregation object and different associated objects into the same object community, and the aggregation effect is positively correlated with the module increase degree. Therefore, if the module increase degree corresponding to the target associated object is the largest and positive, it means that in the i+1th aggregation, aggregating the object to be analyzed corresponding to the i+1th aggregation and the target associated object into the same object community can bring the best object aggregation effect, so that the computer device can complete the i+1th aggregation based on this. In this way, the computer device can use a single object to be analyzed as the aggregation granularity, perform detailed and reasonable object aggregation on multiple objects to be analyzed, and obtain more accurate aggregation results.

[0180] It should be emphasized that if there is no aggregation method with a positive module increase degree in the i+1th aggregation, it means that no matter how the aggregation is performed, the modularity corresponding to the interaction topology will not increase. The interaction topology has achieved the best aggregation effect. At this time, the computer device can directly determine the previous i aggregations as a complete object aggregation process, that is, N=i at this time.

[0181] The modularity corresponding to the interaction topology can be determined in the following ways:

[0182] The computer device may first determine the connection weights corresponding to the connection identifiers in the interactive topology, and the connection weights are used to characterize the closeness of the interaction between the objects to be analyzed that are respectively corresponding to the two object nodes connected by the corresponding connection identifiers. The weight determination method may include multiple methods. For example, the computer device may only consider the presence or absence of interactive behavior, that is, the connection weight of each connection identifier may be the same weight. At this time, the degree of excess represented by the modularity is the degree of excess of the number of connection identifiers between object nodes in the same object community over the number of connection identifiers between object nodes in different object communities; the computer device may also consider the closeness of the interactive behavior based on the presence or absence of interactive behavior, and assign different connection weights to different connection identifiers. For example, the closer the interactive relationship between the two objects to be analyzed, the higher the connection weights can be assigned to the connection identifiers between the object nodes corresponding to the two objects to be analyzed, so that the objects to be analyzed with close interaction can be more accurately aggregated into the same object community based on the modularity. A variety of weight analysis methods can be applied to the technical scenarios of this application, which are not limited here.

[0183] Then, the computer device can determine, in the interactive topology, a first total connection weight corresponding to the connection identifiers between the object nodes included in the community to be analyzed, and a second total connection weight corresponding to the connection identifiers between the object nodes included in the community to be analyzed and the object nodes not included in the community to be analyzed. The community to be analyzed can be any object community in the interactive topology. The first total connection weight can be the sum of the connection weights corresponding to the connection identifiers between the object nodes included in the community to be analyzed, which can reflect the degree of interaction between the objects to be analyzed included in the community to be analyzed; the second total connection weight can be the sum of the connection weights of the connection identifiers between the object nodes included in the community to be analyzed and the object nodes not included in the community to be analyzed, which can reflect the degree of interaction between the objects to be analyzed included in the community to be analyzed and the objects to be analyzed not included in the community to be analyzed.

[0184] Thus, the degree to which the first total connection weight exceeds the second total connection weight can characterize the degree to which the interaction closeness between the multiple objects to be analyzed in the community to be analyzed exceeds the interaction closeness between the objects to be analyzed outside the community to be analyzed and the objects to be analyzed in the community to be analyzed. The computer device can determine the submodule degree corresponding to the community to be analyzed according to the degree to which the first total connection weight exceeds the second total connection weight, and the submodule degree is used to characterize the degree to which the interaction closeness between the multiple objects to be analyzed in the community to be analyzed exceeds the interaction closeness between the objects to be analyzed outside the community to be analyzed and the objects to be analyzed in the community to be analyzed.

[0185] Furthermore, the computer device may determine the modularity corresponding to the interactive topology according to the sum of the sub-modularities corresponding to the communities to be analyzed in the interactive topology.

[0186] The calculation formula of modularity Q can be shown as follows, in which the weight of each connection identifier is 1:

[0187]

[0188] in is the set of object communities, m c is the number of connection identifiers in community c, e c is the number of connection identifiers of object nodes in community c connecting to object nodes in other communities, and m is the total number of connection identifiers in the interactive topology. After experimental verification, the object aggregation method in this application can avoid the problem of generating super-large-scale object communities, and the nodes between communities will not overlap. Figure 5 It can be seen that the object community size obtained by the object aggregation method of the present application is moderate and more in line with the actual interaction situation. Finally, the community division result C of multiple object nodes corresponding to the N objects to be analyzed can be obtained. i}, i = 1, 2, ..., N, where c i ∈{1,2,…,K} represents the object community corresponding to object node i.

[0189] In addition to analyzing the interaction parameters in combination with the community size, the overall interaction closeness of the object community in which the object to be analyzed is located can also, to a certain extent, affect the overall corresponding interaction parameters of the object to be analyzed among multiple objects to be analyzed.

[0190] For example, if the interaction closeness of each object to be analyzed in the target community is relatively low among multiple objects to be analyzed, but relatively speaking, these objects to be analyzed have a high interaction closeness, so they will be aggregated into the same object community. Even if the target object to be analyzed has a certain interaction closeness in the target community, since the interaction closeness of the objects to be analyzed that can be affected by the target object to be analyzed through interaction is relatively low, in fact, from the perspective of the overall multiple objects to be analyzed, the interaction closeness of the object to be analyzed is still relatively low.

[0191] Based on this, in a possible implementation, the computer device can determine the overall interaction parameter corresponding to the target community based on the community interaction parameters corresponding to the objects to be analyzed included in the target community, and the overall interaction parameter is used to characterize the overall interaction closeness of the objects to be analyzed included in the target community. The higher the overall interaction parameter, the greater the impact of the object interaction of the object to be analyzed in the target community on multiple objects to be analyzed, so the target object to be analyzed will also have a higher interaction closeness among multiple objects to be analyzed. The overall interaction parameter can be the sum of the community interaction parameters corresponding to the objects to be analyzed included in the target community, or it can be the average value of the community interaction parameters corresponding to the objects to be analyzed included in the target community.

[0192] When executing step S205, the computer device may execute step S2051 (not shown in the figure), which is a possible implementation of step S205, including:

[0193] S2051: Determine the interaction parameters corresponding to the target object to be analyzed based on the overall interaction parameters, the community parameters, and the community interaction parameters corresponding to the target object to be analyzed.

[0194] Among them, the interaction closeness represented by the interaction parameters is positively correlated with the interaction closeness represented by the overall interaction parameters. Therefore, when the overall interaction closeness of the object community in which the target object to be analyzed is located is high, the target object to be analyzed will also have a higher interaction parameter, which fits the actual situation that the target object to be analyzed has a higher interaction influence among multiple objects to be analyzed, further improves the rationality of the interaction parameters, and subsequently carries out a series of related interaction activities based on the interaction parameters.

[0195] For example, the present application can perform the following interactive activities based on the interactive parameters.

[0196] In a possible implementation, a computer device may initiate a specific interaction. To promote objects to participate in the interaction, the computer device needs to select objects with a greater probability of having the specific interaction.

[0197] Since the interaction parameter can reflect the interaction closeness of the object to be analyzed among multiple objects to be analyzed, generally, the object with higher interaction closeness is more likely to interact. Therefore, the computer device can determine the M objects to be interacted with with the greatest interaction closeness among multiple objects to be analyzed, except for the target object to be analyzed, according to the interaction parameters corresponding to the multiple objects to be analyzed. These M objects to be interacted with can be considered to be the objects most likely to interact with the target object to be analyzed among multiple objects to be analyzed to a certain extent. Therefore, in order to encourage the first target interaction between objects, the computer device can determine the M objects to be interacted with as the set of objects to be interacted with corresponding to the target object to be analyzed. The target object to be analyzed has the authority to initiate the first target interaction to the M objects to be interacted with in the target application. M is a positive integer, and the multiple objects to be analyzed are objects using the target application. In this way, the probability of the first target interaction between objects can be increased, which is helpful for the promotion and dissemination of the first target interaction in the target application. The target application can be any application that can support the first target interaction, and the first target interaction can be any interaction completed by multiple objects.

[0198] For example, in order to encourage players to play team games in a game application, the computer device can determine the M players who interact most closely with the target player in the above manner, and provide the target player with the authority to play team games with the M players, thereby increasing the probability of the target player playing team games and helping to increase the activity of the game application.

[0199] Specifically, in order to facilitate the interaction between objects, in terms of product performance, the computer device can display the corresponding interface of the object to be interacted with in the target application through the terminal device corresponding to the target object to be analyzed, and the interface of the object to be interacted with includes object controls corresponding to M objects to be interacted with. If the target object to be analyzed wants to perform a first target interaction with the target object to be interacted with, a trigger operation can be performed on the object control corresponding to the target object to be interacted with, for example, the object control corresponding to the target object to be interacted with can be clicked.

[0200] In response to obtaining a trigger operation for an object control corresponding to a target object to be interacted with, a computer device may initiate a first target interaction in a target application through a target object to be analyzed and a target object to be interacted with, where the target object to be interacted with is any one of the M objects to be interacted with.

[0201] like Figure 6 As shown, the first target interaction can be to send a greeting card to an object in a game application to attract the object to play the game application. When the target object to be analyzed wants to send a greeting card to object 4, the target object to be analyzed can first click the object control corresponding to object 4, and then click the confirmation control to send a greeting card to object 4. Among them, objects 1 to 8 are the eight objects to be analyzed with the greatest degree of interaction in the set of objects to be analyzed.

[0202] In another possible implementation, the interactive activity may be an activity for obtaining specific permissions through interaction, such as an activity for obtaining permissions to receive gifts or game props through interaction.

[0203] In this implementation, the computer device can determine the P objects to be analyzed with the greatest degree of interaction among the multiple objects to be analyzed based on the interaction parameters corresponding to the multiple objects to be analyzed. Therefore, these P objects to be analyzed are also the objects to be analyzed that other objects to be analyzed are more inclined to interact with. In this interactive activity, the computer device can first grant the P objects to be analyzed target permissions through the target application. The target permissions can be propagated through the interaction between objects. For example, if an object interacts with an object with the target permission, the object will also obtain the target permission. Therefore, in order to promote the propagation of the target permission between objects and promote the interaction between objects, the computer device can determine the P objects to be analyzed as the source of propagation of the target permission. Since the probability of interaction between other objects and the P objects to be analyzed is relatively high, the probability of the target permission being propagated is relatively high, and it is easier to promote.

[0204] The computer device can detect whether the target object completes a specific interaction corresponding to the target permission with the object having the target permission. In response to the target object completing a second target interaction corresponding to the target permission with the object having the target permission in the target application, the computer device can grant the target object the target permission, the target object and the multiple objects to be analyzed are all objects using the target application, the target object can be any object that does not have the target permission, the target permission can be any permission supported by the target application, the target application can be any application that supports the target permission, and the second target interaction can be any interaction in the target application that requires multiple objects to complete.

[0205] In terms of product performance, if the target object wants to obtain the target permission, it can perform a request operation for the target permission, such as clicking on the relevant control in the target application. In response to the request operation for obtaining the target permission of the target object, the computer device can display the permission object interface to the target object through the terminal device corresponding to the target object, and the permission object interface includes object controls corresponding to P objects to be analyzed. If the target object wants to interact with one of the objects to be analyzed to obtain the target permission, it can trigger the object control corresponding to the object to be analyzed.

[0206] In response to obtaining a trigger operation for a target object control, the computer device may initiate a second target interaction in the target application through the target object and the object to be analyzed corresponding to the target object control. The target object control may be any one of the object controls corresponding to the P objects to be analyzed.

[0207] like Figure 7 As shown, the target application may be a game application. In the game application, the target permission may be the permission to obtain specific equipment. The second target interaction is to complete a game. The computer device may first display a permission acquisition interface to the target object. The permission acquisition interface includes a permission acquisition control. The target object may perform the permission acquisition operation by clicking the permission acquisition control.

[0208] At this time, the computer device can display the permission object interface to the target object, which includes object controls corresponding to four objects, namely Object A to Object D. These four objects are objects with the target permission. If the target object clicks the object control corresponding to Object D, and clicks the confirmation control, the computer device can determine to obtain the trigger operation for the object control of Object D, thereby actively starting a game between the target object and Object D, so that the target object can obtain the target permission by completing the game.

[0209] In order to facilitate understanding of the technical solution provided by the embodiment of the present application, the data processing method provided by the embodiment of the present application will be generally introduced below in combination with an actual application scenario.

[0210] See also Figure 8 , Figure 8 A flowchart of a data processing method in an actual application scenario provided in an embodiment of the present application. In this actual application scenario, the target application may be a game application, the object to be analyzed may be a player of the game application, and the computer device may be a server of the game application.

[0211] The method includes:

[0212] S801: Get game log

[0213] The game log is used to record the interactions between game players.

[0214] S802: Generate interaction topology based on game logs

[0215] Based on the game logs, the computer device can construct an interactive topology corresponding to multiple players. The nodes represent each player in the game, and the edges represent the interactive relationship between players. In this actual application scenario, the edges can indicate that the players have a game friend relationship. The computer device can randomly select N players, and then extract the players' friend relationship logs and team game logs in the past three months to obtain the interactive topology graph G = (V, E), where V (|V| = N) is a collection of nodes. is a set of edges, each edge v ij ∈E indicates that the players corresponding to nodes i and j are friends.

[0216] S803: Aggregate based on the interactive topology to generate multiple player communities.

[0217] S804: Performing a node centrality algorithm analysis in k dimensions based on each player community to determine the sub-interaction parameters corresponding to the players in the k dimensions.

[0218] In this actual application scenario, the multiple dimensions may be k node centrality algorithm dimensions, and the sub-interaction parameters may be parameters calculated by the players through the node centrality algorithms under k dimensions. For example, five node centrality algorithms (k=5) including degree, H index, W index, k-core index, and collaborative influence may be used to obtain the sub-interaction parameters corresponding to each object to be analyzed in the five dimensions. The following is an introduction to the determination of each sub-interaction parameter:

[0219] Degree: The number of first-order friends of the object node in the interaction topology, that is, the number of nodes directly connected to the object node through connection relationships.

[0220] H-index: In the interactive topology, the H-index of an object node is h, which means that among the object nodes directly connected to it, there are h object nodes with a degree of at least h, and the degrees of the remaining directly connected object nodes are all less than (h+1).

[0221] W-index: A W-index of an object node equal to w means that among the object nodes directly connected to it, w object nodes have a degree of at least 10w, and the degrees of the remaining object nodes directly connected to it are all less than 10(w+1). Compared with H-index, W-index is a stronger filter, preferring neighbor nodes with larger degrees (i.e., object nodes directly connected to it).

[0222] K-core index: If an object node exists in the k-core subgraph and is removed from the (k+1)core subgraph, the K-core index of this object node is k. If a graph is a KCore subgraph, the degree of the object node of each vertex in this subgraph is ≥ K. The larger k is, the more core the object node is in the network structure, so its structural importance is greater and the degree of interaction in the interaction topology is greater.

[0223] Collaborative influence: The collaborative influence of an object node is equivalent to the product of the degree of the object node and the sum of the degrees of its l-th order neighbor nodes (i.e., object nodes that require l connections to be connected to the object node), where l is a hyperparameter. In this application, l=3 can be set.

[0224] S805: Determine the optimal solution and the worst solution corresponding to each player community.

[0225] S806: Perform relative entropy calculation based on the optimal solution and the worst solution to determine the community interaction parameters corresponding to each player.

[0226] The parameters calculated by relative entropy can reflect the closeness between the sub-interaction parameters and the maximum value.

[0227] S807: Determine interaction parameters based on the community size and community interaction parameters of the player community in which the player is located.

[0228] S808: Determine the players with the target authority based on the interaction parameters.

[0229] Other players who have not been given the target permission can obtain the target permission by performing specified interactions with players who have the target permission (such as playing a game, adding friends, etc. to obtain the target permission). The target permission is used to obtain specific rewards, such as in-game equipment, numerical rewards, etc.

[0230] Through verification, the present application obtained the data table shown below, wherein the present application adopted four different target permission delivery methods for testing, namely, delivery based only on interaction parameters (interaction parameters calculated without community segmentation), delivery based on community interaction parameters and community size, delivery based on community interaction parameters and parameter weights, and delivery based on community interaction parameters, community size and parameter weights, and obtained the number of players disseminating target permissions under different delivery methods. As can be seen from the table, the interaction parameter analysis method combining community interaction parameters and community size / parameter weights in the present application can significantly increase the number of players disseminating target permissions compared to the interaction parameter analysis method directly based on multiple players in the related art, that is, it can accurately analyze players who are easier to interact, thereby significantly improving the player interaction activity of the game application. The combination of the three can further improve the interaction effect.

[0231]

[0232] Based on the data processing method provided in the above embodiment, the present application embodiment also provides a data processing device, see Fig. 9 , Fig. 9 The structural block diagram of a data processing device 900 provided in an embodiment of the present application, the device 900 includes an acquisition unit 901, an aggregation unit 902, a first determination unit 903, a second determination unit 904 and a third determination unit 905:

[0233] The acquisition unit 901 is used to acquire a set of objects to be analyzed, where the set to be analyzed includes a plurality of objects to be analyzed;

[0234] The aggregation unit 902 is used to aggregate the multiple objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed, and generate multiple object communities corresponding to the set of objects to be analyzed, wherein the interaction closeness between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the interaction closeness between the objects to be analyzed outside the target community and the target objects to be analyzed, the target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community;

[0235] The first determining unit 903 is used to determine the community interaction parameters corresponding to the objects to be analyzed in the target community according to the interaction relationship between the objects to be analyzed included in the target community, and the community interaction parameters corresponding to the target objects to be analyzed are used to characterize the interaction closeness between the target objects to be analyzed and other objects to be analyzed included in the target community;

[0236] The second determining unit 904 is used to determine a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community, wherein the community parameter is used to characterize the community scale of the target community;

[0237] The third determination unit 905 is used to determine the interaction parameters corresponding to the target object to be analyzed based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed, the degree of interaction characterized by the interaction parameters is positively correlated with the community size characterized by the community parameters, and is positively correlated with the degree of interaction characterized by the community interaction parameters corresponding to the target object to be analyzed.

[0238] In a possible implementation manner, the first determining unit 903 is specifically configured to:

[0239] For the target object to be analyzed, according to the interaction relationship between the objects to be analyzed included in the target community, sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions are determined, and the target sub-interaction parameters corresponding to the target object to be analyzed in the target dimension are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the target community under the target dimension, and the target dimension is any one of the multiple dimensions;

[0240] According to the sub-interaction parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed are determined.

[0241] In a possible implementation manner, the apparatus further includes a fourth determining unit:

[0242] The fourth determination unit is used to determine the parameter weight corresponding to the target dimension according to the parameter distribution difference of the sub-interaction parameter of the target dimension in the objects to be analyzed included in the target community, the parameter weight corresponding to the target dimension is used to identify the representation strength of the sub-interaction parameter of the target dimension on the interaction closeness between the target object to be analyzed and other objects to be analyzed included in the target community, and the parameter weight corresponding to the target dimension is positively correlated with the parameter distribution difference;

[0243] The first determining unit 903 is specifically configured to:

[0244] The community interaction parameters corresponding to the target object to be analyzed are determined according to the sub-interaction parameters corresponding to the multiple dimensions of the target object to be analyzed and the parameter weights corresponding to the multiple dimensions.

[0245] In a possible implementation manner, the first determining unit 903 is specifically configured to:

[0246] Determine the proximity parameter of the target object to be analyzed corresponding to the target dimension according to the proportional relationship between the target sub-interaction parameter and the maximum value of the sub-interaction parameter corresponding to the object to be analyzed included in the target community in the target dimension, wherein the proximity parameter is used to identify the proximity between the target sub-interaction parameter and the maximum value, and the maximum value is used to characterize the interaction closeness boundary of the object to be analyzed included in the target community in the target dimension;

[0247] According to the proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, a social interaction parameter corresponding to the target object to be analyzed is determined.

[0248] In a possible implementation, the maximum value includes an optimal value and a worst value, the proximity parameter includes an optimal proximity parameter and a worst proximity parameter, the optimal value is a sub-interaction parameter corresponding to the object to be analyzed with the largest interaction closeness under the target dimension among the objects to be analyzed included in the target community, and the worst value is a sub-interaction parameter corresponding to the object to be analyzed with the smallest interaction closeness under the target dimension among the objects to be analyzed included in the target community, and the first determining unit 903 is specifically used to:

[0249] Determine the optimal proximity degree parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the optimal value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community, and determine the worst proximity degree parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the worst value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community;

[0250] According to the optimal proximity parameters and the worst proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed are determined, and the interaction closeness represented by the community interaction parameters corresponding to the target object to be analyzed is positively correlated with the proximity indicated by the optimal proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, and negatively correlated with the proximity indicated by the worst proximity parameters corresponding to the target object to be analyzed in the multiple dimensions.

[0251] In a possible implementation, the aggregation unit 902 is specifically configured to:

[0252] According to the interaction relationship between the multiple objects to be analyzed, an interaction topology corresponding to the set of objects to be analyzed is constructed, wherein the interaction topology includes object nodes corresponding to the multiple objects to be analyzed respectively, and connection identifiers between the object nodes, and the connection identifier is used to identify that there is an interaction behavior between the objects to be analyzed corresponding to any two object nodes connected by the connection identifier;

[0253] The multiple objects to be analyzed are aggregated based on the interaction topology to generate multiple object communities corresponding to the set of objects to be analyzed.

[0254] In a possible implementation, the object aggregation includes N aggregations, and the aggregation unit 902 is specifically configured to:

[0255] In the (i+1)th aggregation, any object to be analyzed that does not have a corresponding object community after the previous i aggregations is used as the object to be analyzed corresponding to the (i+1)th aggregation, and an associated object corresponding to the object to be analyzed corresponding to the (i+1)th aggregation is determined, where the associated object is an object having a connection identifier between corresponding object nodes in the interaction topology with the object to be analyzed corresponding to the (i+1)th aggregation;

[0256] Determine the module increase degrees respectively corresponding to the associated objects, wherein the module increase degree corresponding to the target associated object is used to identify the increase value of the module degree corresponding to the interaction topology after the object to be analyzed corresponding to the i+1th aggregation and the target associated object are aggregated into the same object community, the module degree is used to reflect the degree of excess of the first interaction closeness over the second interaction closeness in the interaction topology, the first interaction closeness is the interaction closeness between multiple objects to be analyzed in the same object community, the second interaction closeness is the interaction closeness between multiple objects to be analyzed in different object communities, the module degree is positively correlated with the excess degree, and the target associated object is any one of the associated objects;

[0257] In response to the module increase degree corresponding to the target-associated object being the largest and being a positive value, the object to be analyzed corresponding to the i+1th aggregation and the target-associated object are aggregated into the same object community to complete the i+1th aggregation, where N is a positive integer greater than 1 and i is a positive integer less than N.

[0258] In a possible implementation, the modularity corresponding to the interaction topology is determined in the following manner:

[0259] Determine the connection weights corresponding to the connection identifiers in the interaction topology, wherein the connection weights are used to characterize the interaction closeness between the objects to be analyzed corresponding to the two object nodes connected by the corresponding connection identifiers;

[0260] Determine, in the interactive topology, a first total connection weight corresponding to connection identifiers between object nodes included in the community to be analyzed, and a second total connection weight corresponding to connection identifiers between object nodes included in the community to be analyzed and object nodes not included in the community to be analyzed, wherein the community to be analyzed is any object community in the interactive topology;

[0261] Determining a submodule degree corresponding to the community to be analyzed according to the degree to which the first total connection weight exceeds the second total connection weight;

[0262] The modularity corresponding to the interactive topology is determined according to the sum of the sub-modularity corresponding to the communities to be analyzed in the interactive topology.

[0263] In a possible implementation manner, the apparatus further includes a fifth determining unit:

[0264] The fifth determining unit is used to determine the overall interaction parameter corresponding to the target community based on the community interaction parameters corresponding to the objects to be analyzed included in the target community, wherein the overall interaction parameter is used to characterize the overall interaction closeness of the objects to be analyzed included in the target community;

[0265] The third determining unit 905 is specifically configured to:

[0266] Based on the overall interaction parameter, the community parameter and the community interaction parameter corresponding to the target object to be analyzed, the interaction parameter corresponding to the target object to be analyzed is determined, and the interaction closeness represented by the interaction parameter is positively correlated with the interaction closeness represented by the overall interaction parameter.

[0267] In a possible implementation manner, the apparatus further includes a sixth determining unit and a seventh determining unit:

[0268] The sixth determining unit is used to determine, according to the interaction parameters respectively corresponding to the multiple objects to be analyzed, M objects to be interacted with that have the greatest degree of interaction among the multiple objects to be analyzed, except the target object to be analyzed;

[0269] The seventh determination unit is used to determine the M objects to be interacted with as a set of objects to be interacted with corresponding to the target object to be analyzed, the target object to be analyzed has the authority to initiate a first target interaction to the M objects to be interacted with in a target application, M is a positive integer, and the multiple objects to be analyzed are objects using the target application.

[0270] In a possible implementation, the device further includes a first display unit and a first response unit:

[0271] The first display unit is used to display the object interface corresponding to the set of objects to be interacted in the target application through the terminal device corresponding to the target object to be analyzed, and the object interface includes object controls corresponding to the M objects to be interacted respectively;

[0272] The first response unit is used to initiate the first target interaction in the target application through the target object to be analyzed and the target object to be interacted with in response to obtaining a trigger operation for the object control corresponding to the target object to be interacted with, and the target object to be interacted with is any one of the M objects to be interacted with.

[0273] In a possible implementation manner, the device further includes an eighth determining unit, a permission unit, and a second responding unit:

[0274] The eighth determination unit is used to determine P objects to be analyzed having the greatest interaction closeness among the multiple objects to be analyzed according to the interaction parameters respectively corresponding to the multiple objects to be analyzed;

[0275] The permission unit is used to grant target permissions to the P objects to be analyzed through a target application;

[0276] The second response unit is used to grant the target object the target permission in response to the target object and the object with the target permission completing the second target interaction corresponding to the target permission in the target application, and the target object and the multiple objects to be analyzed are all objects using the target application.

[0277] In a possible implementation manner, the device further includes a third response unit and a fourth response unit:

[0278] The third response unit is configured to display a permission object interface to the target object through a terminal device corresponding to the target object in response to obtaining a request operation of the target object for the target permission, wherein the permission object interface includes object controls corresponding to the P objects to be analyzed respectively;

[0279] The fourth response unit is used to initiate the second target interaction in the target application through the target object and the object to be analyzed corresponding to the target object control in response to obtaining a trigger operation for the target object control, and the target object control is any one of the object controls corresponding to the P objects to be analyzed.

[0280] The present application also provides a computer device, see Fig.10 As shown, the computer device may be a terminal device, and a mobile phone is taken as an example:

[0281] Fig.10 FIG. 1 is a block diagram showing a partial structure of a mobile phone related to a terminal device provided in an embodiment of the present application. Fig.10 The mobile phone includes: a radio frequency (RF) circuit 710, a memory 720, an input unit 730, a display unit 740, a sensor 750, an audio circuit 760, a wireless fidelity (WiFi) module 770, a processor 780, and a power supply 790. Those skilled in the art will understand that Fig.10 The mobile phone structure shown in the figure does not constitute a limitation on the mobile phone, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0282] Combine the following Fig.10A detailed introduction to the various components of the mobile phone:

[0283] The RF circuit 710 can be used for receiving and sending signals during the process of sending and receiving information or making calls. In particular, after receiving the downlink information of the base station, it is sent to the processor 780 for processing; in addition, the designed uplink data is sent to the base station. Usually, the RF circuit 710 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (Low Noise Amplifier, referred to as LNA), a duplexer, etc. In addition, the RF circuit 710 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to the Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0284] The memory 720 can be used to store software programs and modules. The processor 780 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 720. The memory 720 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 720 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0285] The input unit 730 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the mobile phone. Specifically, the input unit 730 may include a touch panel 731 and other input devices 732. The touch panel 731, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 731 or near the touch panel 731 using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 731 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 780, and can receive and execute commands sent by the processor 780. In addition, the touch panel 731 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 731, the input unit 730 may further include other input devices 732. Specifically, the other input devices 732 may include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

[0286] The display unit 740 can be used to display information input by the user or information provided to the user and various menus of the mobile phone. The display unit 740 may include a display panel 741. Optionally, the display panel 741 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an organic light-emitting diode (Organic Light-Emitting Diode, OLED), etc. Further, the touch panel 731 may cover the display panel 741. When the touch panel 731 detects a touch operation on or near it, it is transmitted to the processor 780 to determine the type of touch event. Subsequently, the processor 780 provides a corresponding visual output on the display panel 741 according to the type of touch event. Although in Fig.10 In the embodiment, the touch panel 731 and the display panel 741 are used as two independent components to realize the input and output functions of the mobile phone, but in some embodiments, the touch panel 731 and the display panel 741 can be integrated to realize the input and output functions of the mobile phone.

[0287] The mobile phone may also include at least one sensor 750, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 741 according to the brightness of the ambient light, and the proximity sensor may turn off the display panel 741 and / or the backlight when the mobile phone is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that can be configured in the mobile phone, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0288] The audio circuit 760, the speaker 761, and the microphone 762 can provide an audio interface between the user and the mobile phone. The audio circuit 760 can transmit the received audio data to the speaker 761 after converting the received audio data into an electrical signal, which is converted into a sound signal for output; on the other hand, the microphone 762 converts the collected sound signal into an electrical signal, which is received by the audio circuit 760 and converted into audio data, and then the audio data is output to the processor 780 for processing, and then sent to another mobile phone through the RF circuit 710, or the audio data is output to the memory 720 for further processing.

[0289] WiFi is a short-range wireless transmission technology. The mobile phone can help users send and receive emails, browse web pages and access streaming media through the WiFi module 770. It provides users with wireless broadband Internet access. Fig.10 A WiFi module 770 is shown, but it is understandable that it is not an essential component of the mobile phone and can be omitted as needed without changing the essence of the invention.

[0290] The processor 780 is the control center of the mobile phone. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 720, and calling data stored in the memory 720, it executes various functions of the mobile phone and processes data, thereby performing overall detection of the mobile phone. Optionally, the processor 780 may include one or more processing units; preferably, the processor 780 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 780.

[0291] The mobile phone also includes a power supply 790 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 780 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions.

[0292] Although not shown, the mobile phone may also include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0293] In this embodiment, the processor 780 included in the terminal device also has the following functions:

[0294] Acquire a set of objects to be analyzed, wherein the set to be analyzed includes a plurality of objects to be analyzed;

[0295] Based on the interaction relationship between the multiple objects to be analyzed, the multiple objects to be analyzed are aggregated to generate multiple object communities corresponding to the set of objects to be analyzed, the interaction closeness between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the interaction closeness between the objects to be analyzed outside the target community and the target objects to be analyzed, the target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community;

[0296] According to the interaction relationship between the objects to be analyzed included in the target community, determining the community interaction parameters corresponding to the objects to be analyzed in the target community, wherein the community interaction parameters corresponding to the target objects to be analyzed are used to characterize the degree of interaction between the target objects to be analyzed and other objects to be analyzed included in the target community;

[0297] Determining a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community, wherein the community parameter is used to characterize the community scale of the target community;

[0298] Based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters corresponding to the target object to be analyzed are determined, the interaction parameters are used to characterize the degree of interaction closeness between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed, the degree of interaction closeness characterized by the interaction parameters is positively correlated with the community size characterized by the community parameters, and is positively correlated with the degree of interaction closeness characterized by the community interaction parameters corresponding to the target object to be analyzed.

[0299] The present application also provides a server. Fig.11 As shown, Fig.11The structural diagram of the server 800 provided in the embodiment of the present application, the server 800 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 822 (for example, one or more processors) and a memory 832, and one or more storage media 830 (for example, one or more mass storage devices) storing application programs 842 or data 844. Among them, the memory 832 and the storage medium 830 can be temporary storage or permanent storage. The program stored in the storage medium 830 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Furthermore, the central processing unit 822 can be configured to communicate with the storage medium 830 and execute a series of instruction operations in the storage medium 830 on the server 800.

[0300] The server 800 may also include one or more power supplies 826, one or more wired or wireless network interfaces 850, one or more input and output interfaces 858, and / or one or more operating systems 841, such as Windows Server 2000. TM , Mac OS X TM , Unix TM ,Linux TM , FreeBSD TM etc.

[0301] The steps performed by the server in the above embodiment can be based on Fig.11 The server structure shown.

[0302] An embodiment of the present application also provides a computer-readable storage medium for storing a computer program, wherein the computer program is used to execute any one of the data processing methods described in the aforementioned embodiments.

[0303] An embodiment of the present application further provides a computer program product including a computer program, which, when executed on a computer device, enables the computer device to execute the data processing method described in any one of the above embodiments.

[0304] It is understandable that in the specific implementation of the present application, related data such as user information (such as player logs) are involved. When the above embodiments of the present application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0305] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the above-mentioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the above-mentioned storage medium can be at least one of the following media: read-only memory (English: read-only memory, abbreviated: ROM), RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0306] It should be noted that each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0307] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire a set of objects to be analyzed, wherein the set to be analyzed includes a plurality of objects to be analyzed; Based on the interaction relationship between the multiple objects to be analyzed, the multiple objects to be analyzed are aggregated to generate multiple object communities corresponding to the set of objects to be analyzed, the interaction closeness between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the interaction closeness between the objects to be analyzed outside the target community and the target objects to be analyzed, the target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community; According to the interaction relationship between the objects to be analyzed included in the target community, determining the community interaction parameters corresponding to the objects to be analyzed in the target community, wherein the community interaction parameters corresponding to the target objects to be analyzed are used to characterize the degree of interaction between the target objects to be analyzed and other objects to be analyzed included in the target community; Determining a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community, wherein the community parameter is used to characterize the community scale of the target community; Based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters corresponding to the target object to be analyzed are determined, the interaction parameters are used to characterize the degree of interaction closeness between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed, the degree of interaction closeness characterized by the interaction parameters is positively correlated with the community size characterized by the community parameters, and is positively correlated with the degree of interaction closeness characterized by the community interaction parameters corresponding to the target object to be analyzed.

2. The method according to claim 1, characterized in that The determining, according to the interaction relationship between the objects to be analyzed included in the target community, community interaction parameters corresponding to the objects to be analyzed in the target community respectively includes: For the target object to be analyzed, according to the interaction relationship between the objects to be analyzed included in the target community, sub-interaction parameters corresponding to the target object to be analyzed in multiple dimensions are determined, and the target sub-interaction parameters corresponding to the target object to be analyzed in the target dimension are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the target community under the target dimension, and the target dimension is any one of the multiple dimensions; According to the sub-interaction parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed are determined.

3. The method according to claim 2, characterized in that The method further comprises: Determine the parameter weight corresponding to the target dimension according to the parameter distribution difference of the sub-interaction parameter of the target dimension in the objects to be analyzed included in the target community, the parameter weight corresponding to the target dimension is used to identify the strength of the sub-interaction parameter of the target dimension in representing the closeness of the interaction between the target object to be analyzed and other objects to be analyzed included in the target community, and the parameter weight corresponding to the target dimension is positively correlated with the parameter distribution difference; The determining, according to the sub-interaction parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed includes: The community interaction parameters corresponding to the target object to be analyzed are determined according to the sub-interaction parameters corresponding to the multiple dimensions of the target object to be analyzed and the parameter weights corresponding to the multiple dimensions.

4. The method according to claim 2, characterized in that: The determining, according to the sub-interaction parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed includes: Determine the proximity parameter of the target object to be analyzed corresponding to the target dimension according to the proportional relationship between the target sub-interaction parameter and the maximum value of the sub-interaction parameter corresponding to the object to be analyzed included in the target community in the target dimension, wherein the proximity parameter is used to identify the proximity between the target sub-interaction parameter and the maximum value, and the maximum value is used to characterize the interaction closeness boundary of the object to be analyzed included in the target community in the target dimension; According to the proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, a social interaction parameter corresponding to the target object to be analyzed is determined.

5. The method according to claim 4, characterized in that The maximum value includes an optimal value and a worst value, and the proximity parameter includes an optimal proximity parameter and a worst proximity parameter, wherein the optimal value is a sub-interaction parameter corresponding to the object to be analyzed with the largest interaction closeness in the target dimension among the objects to be analyzed included in the target community, and the worst value is a sub-interaction parameter corresponding to the object to be analyzed with the smallest interaction closeness in the target dimension among the objects to be analyzed included in the target community, and determining the proximity parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the maximum value of the sub-interaction parameter corresponding to the object to be analyzed included in the target community in the target dimension includes: Determine the optimal proximity degree parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the optimal value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community, and determine the worst proximity degree parameter of the target object to be analyzed in the target dimension according to the proportional relationship between the target sub-interaction parameter and the worst value of the sub-interaction parameter corresponding to the target dimension of the object to be analyzed included in the target community; The determining, according to the proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, the social interaction parameters corresponding to the target object to be analyzed includes: According to the optimal proximity parameters and the worst proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, the community interaction parameters corresponding to the target object to be analyzed are determined, and the interaction closeness represented by the community interaction parameters corresponding to the target object to be analyzed is positively correlated with the proximity indicated by the optimal proximity parameters corresponding to the target object to be analyzed in the multiple dimensions, and negatively correlated with the proximity indicated by the worst proximity parameters corresponding to the target object to be analyzed in the multiple dimensions.

6. The method according to claim 1, characterized in that The step of aggregating the multiple objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed to generate multiple object communities corresponding to the set of objects to be analyzed includes: According to the interaction relationship between the multiple objects to be analyzed, construct an interaction topology corresponding to the set of objects to be analyzed, wherein the interaction topology includes object nodes corresponding to the multiple objects to be analyzed respectively, and connection identifiers between the object nodes, and the connection identifier is used to identify that there is an interaction behavior between the objects to be analyzed corresponding to any two object nodes connected by the connection identifier; The multiple objects to be analyzed are aggregated based on the interaction topology to generate multiple object communities corresponding to the set of objects to be analyzed.

7. The method according to claim 6, characterized in that The object aggregation includes N aggregations, and the object aggregation of the multiple objects to be analyzed based on the interaction topology to generate multiple object communities corresponding to the set of objects to be analyzed includes: In the (i+1)th aggregation, any object to be analyzed that does not have a corresponding object community after the previous i aggregations is used as the object to be analyzed corresponding to the (i+1)th aggregation, and an associated object corresponding to the object to be analyzed corresponding to the (i+1)th aggregation is determined, where the associated object is an object having a connection identifier between corresponding object nodes in the interaction topology with the object to be analyzed corresponding to the (i+1)th aggregation; Determine the module increase degrees respectively corresponding to the associated objects, wherein the module increase degree corresponding to the target associated object is used to identify the increase value of the module degree corresponding to the interaction topology after the object to be analyzed corresponding to the i+1th aggregation and the target associated object are aggregated into the same object community, the module degree is used to reflect the degree of excess of the first interaction closeness over the second interaction closeness in the interaction topology, the first interaction closeness is the interaction closeness between multiple objects to be analyzed in the same object community, the second interaction closeness is the interaction closeness between multiple objects to be analyzed in different object communities, the module degree is positively correlated with the excess degree, and the target associated object is any one of the associated objects; In response to the module increase degree corresponding to the target-associated object being the largest and being a positive value, the object to be analyzed corresponding to the i+1th aggregation and the target-associated object are aggregated into the same object community to complete the i+1th aggregation, where N is a positive integer greater than 1 and i is a positive integer less than N.

8. The method according to claim 7, characterized in that The modularity corresponding to the interaction topology is determined in the following way: Determine the connection weights corresponding to the connection identifiers in the interaction topology, wherein the connection weights are used to characterize the interaction closeness between the objects to be analyzed corresponding to the two object nodes connected by the corresponding connection identifiers; Determine, in the interactive topology, a first total connection weight corresponding to connection identifiers between object nodes included in the community to be analyzed, and a second total connection weight corresponding to connection identifiers between object nodes included in the community to be analyzed and object nodes not included in the community to be analyzed, wherein the community to be analyzed is any object community in the interactive topology; Determining a submodule degree corresponding to the community to be analyzed according to the degree to which the first total connection weight exceeds the second total connection weight; The modularity corresponding to the interactive topology is determined according to the sum of the sub-modularity corresponding to the communities to be analyzed in the interactive topology.

9. The method according to claim 1, characterized in that: The method further comprises: Based on the community interaction parameters corresponding to the objects to be analyzed included in the target community, respectively, determining the overall interaction parameter corresponding to the target community, wherein the overall interaction parameter is used to characterize the overall interaction closeness of the objects to be analyzed included in the target community; The determining, based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters corresponding to the target object to be analyzed includes: Based on the overall interaction parameter, the community parameter and the community interaction parameter corresponding to the target object to be analyzed, the interaction parameter corresponding to the target object to be analyzed is determined, and the interaction closeness represented by the interaction parameter is positively correlated with the interaction closeness represented by the overall interaction parameter.

10. The method according to claim 1, characterized in that The method further comprises: According to the interaction parameters respectively corresponding to the multiple objects to be analyzed, determining M objects to be interacted with, except the target object to be analyzed, with the greatest degree of interaction among the multiple objects to be analyzed; The M objects to be interacted with are determined as a set of objects to be interacted with corresponding to the target object to be analyzed, the target object to be analyzed has the authority to initiate a first target interaction to the M objects to be interacted with in a target application, M is a positive integer, and the multiple objects to be analyzed are objects using the target application.

11. The method according to claim 10, characterized in that The method further comprises: Displaying the object interface corresponding to the set of objects to be interacted with in the target application through the terminal device corresponding to the target object to be analyzed, wherein the object interface includes object controls corresponding to the M objects to be interacted with respectively; In response to obtaining a trigger operation for an object control corresponding to a target object to be interacted with, the first target interaction is initiated in the target application through the target object to be analyzed and the target object to be interacted with, and the target object to be interacted with is any one of the M objects to be interacted with.

12. The method according to claim 1, characterized in that The method further comprises: Determine, according to the interaction parameters respectively corresponding to the multiple objects to be analyzed, P objects to be analyzed having the greatest interaction intimacy among the multiple objects to be analyzed; Granting target permissions to the P objects to be analyzed through a target application; In response to the target object and the object having the target permission completing a second target interaction corresponding to the target permission in the target application, the target object is granted the target permission, and the target object and the multiple objects to be analyzed are all objects using the target application.

13. The method according to claim 12, characterized in that The method further comprises: In response to obtaining a request operation of the target object for the target permission, displaying a permission object interface to the target object through a terminal device corresponding to the target object, wherein the permission object interface includes object controls corresponding to the P objects to be analyzed respectively; In response to obtaining a trigger operation for a target object control, the second target interaction is initiated in the target application through the target object and the object to be analyzed corresponding to the target object control, where the target object control is any one of the object controls corresponding to the P objects to be analyzed.

14. A data processing device, characterized in that: The device includes an acquisition unit, an aggregation unit, a first determination unit, a second determination unit, and a third determination unit: The acquisition unit is used to acquire a set of objects to be analyzed, wherein the set to be analyzed includes a plurality of objects to be analyzed; The aggregation unit is used to aggregate the multiple objects to be analyzed based on the interaction relationship between the multiple objects to be analyzed, and generate multiple object communities corresponding to the set of objects to be analyzed, wherein the interaction closeness between the objects to be analyzed in the target community and the target objects to be analyzed is greater than the interaction closeness between the objects to be analyzed outside the target community and the target objects to be analyzed, the target community is any one of the multiple object communities, and the target object to be analyzed is any one of the objects to be analyzed included in the target community; The first determining unit is used to determine the community interaction parameters corresponding to the objects to be analyzed in the target community according to the interaction relationship between the objects to be analyzed included in the target community, and the community interaction parameters corresponding to the target objects to be analyzed are used to characterize the interaction closeness between the target objects to be analyzed and other objects to be analyzed included in the target community; The second determining unit is used to determine a community parameter corresponding to the target community according to the number of objects to be analyzed included in the target community, wherein the community parameter is used to characterize the community scale of the target community; The third determination unit is used to determine the interaction parameters corresponding to the target object to be analyzed based on the community parameters and the community interaction parameters corresponding to the target object to be analyzed, the interaction parameters are used to characterize the degree of interaction between the target object to be analyzed and other objects to be analyzed included in the set of objects to be analyzed, the degree of interaction characterized by the interaction parameters is positively correlated with the community size characterized by the community parameters, and is positively correlated with the degree of interaction characterized by the community interaction parameters corresponding to the target object to be analyzed.

15. A computer device, characterized in that: The computer device comprises a processor and a memory: The memory is used to store a computer program and transmit the computer program to the processor; The processor is used to execute the data processing method according to any one of claims 1 to 13 according to the instructions in the computer program.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the data processing method according to any one of claims 1 to 13.

17. A computer program product comprising a computer program, which, when executed on a computer device, enables the computer device to execute the data processing method according to any one of claims 1 to 13.

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