A method and device for discovering target communities integrating content structure rules and time laws

Abstract

The present disclosure relates to the technical field of complex network analysis, and specifically to a method and device for discovering a target community that integrates content structure rules and time patterns. The method for discovering a target community that integrates content structure rules and time patterns includes: obtaining a user's posting information and number of posts; identifying the user's content structure rules from the posting information, and calculating the similarity of content structure rules between users using the Jaccard similarity coefficient; constructing a user posting time pattern matrix based on the number of posts, and calculating the similarity of time patterns between users using the Pearson correlation coefficient; establishing a network undirected weighted graph based on the similarity of content structure rules between users and the similarity of time patterns between users; and using a spectral clustering method to perform community discovery on the network undirected weighted graph to obtain community division results. The above technical solution helps to discover hidden communities and influence networks in social platforms and accurately locate target communities based on user preferences.

Description

Technical Field

[0001] The present disclosure relates to the technical field of complex network analysis, and in particular to a method and device for discovering a target community by integrating content structure rules and time regularities. Background Art

[0002] Community detection has become a crucial research topic in complex network analysis. Existing community detection methods can be broadly categorized into two categories: global community detection methods, which analyze the entire complex network, and local community detection methods designed for specific objectives. Global community detection, as a traditional community detection method, is often based on global information. However, as network size continues to grow, obtaining global information becomes increasingly difficult, and the computational complexity involved in obtaining this information is also enormous. Therefore, traditional community detection methods are inefficient in large-scale networks. Local community detection methods, on the other hand, often use seeds as initial communities and expand them through a greedy optimization process using a quality function. Therefore, the quality function and expansion method determine the effectiveness of local community detection methods.

[0003] Although current community detection technology has alleviated the efficiency issues faced by community detection to a certain extent, it often focuses on the topology of the network and pays less attention to users' personalized preferences, making it difficult to accurately locate target communities based on user preferences for specific applications. Summary of the Invention

[0004] In order to solve the problems in the related art, the embodiments of the present disclosure provide a method and apparatus for discovering a target community by integrating content structure rules and time regularities.

[0005] In a first aspect, embodiments of the present disclosure provide a method for discovering target communities that integrates content structure rules and time regularities, including:

[0006] Get the user's posting information and posting times;

[0007] Identifying user content structure rules from the posting information, and calculating the similarity of content structure rules between users using the Jaccard similarity coefficient;

[0008] Build a user posting time regularity matrix based on the number of posts, and calculate the time regularity similarity between users using the Pearson correlation coefficient;

[0009] Establishing a network undirected weighted graph based on the similarity of content structure rules between users and the similarity of time regularity between users;

[0010] A spectral clustering method is used to perform community discovery on the undirected weighted graph of the network to obtain a community division result.

[0011] In one implementation of the present disclosure, identifying the user content structure rule from the posting information includes:

[0012] Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters;

[0013] Recognizing preset pattern characters in the posted information;

[0014] All recognized preset pattern characters are used as user content structure rules.

[0015] In one implementation of the present disclosure, constructing a user posting time regularity matrix based on the posting times includes:

[0016] Count the number of posts in a preset time period;

[0017] Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

[0018] In one implementation of the present disclosure, constructing a user posting time regularity matrix based on the posting times further includes:

[0019] If there is interaction between users, a bias is added to the corresponding element in the user posting time regularity matrix.

[0020] In one implementation of the present disclosure, before calculating the temporal regularity similarity between users using the Pearson correlation coefficient, the method further includes:

[0021] The user posting time regularity matrix is ​​normalized.

[0022] In one implementation of the present disclosure, establishing a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users includes:

[0023] The social network is represented as an undirected unweighted graph; the undirected unweighted graph includes user nodes and edges connecting the user nodes;

[0024] Calculate the weight WJR of the edge in the undirected unweighted graph ij , the formula is as follows:

[0025] WJR ij =αJ(v i ,v j )+(1-α)R(v i ,v j );

[0026] Based on the weights WJR ij and undirected unweighted graphs to build network undirected weighted graphs;

[0027] Among them, α is an adjustable parameter, 0<α<1, J(v i ,v j ) represents user v i With user v j The similarity of content structure rules between users, R(v i ,v j ) represents user v i With user v j The temporal regularity similarity between users.

[0028] In one implementation of the present disclosure, it further includes:

[0029] The modularity of the community is calculated based on the community division result.

[0030] In a second aspect, the embodiments of the present disclosure provide a target community discovery device that integrates content structure rules and time regularities, including:

[0031] An acquisition module is configured to obtain user posting information and posting times;

[0032] A first calculation module is configured to identify user content structure rules from the posting information and calculate the similarity of content structure rules between users using the Jaccard similarity coefficient;

[0033] A second calculation module is configured to construct a user posting time regularity matrix based on the number of posts, and calculate the time regularity similarity between users using the Pearson correlation coefficient;

[0034] A construction module is configured to establish a network undirected weighted graph based on the similarity of content structure rules between users and the similarity of time regularity between users;

[0035] The community discovery module is configured to use a spectral clustering method to perform community discovery on the undirected weighted graph of the network to obtain a community division result.

[0036] In one implementation of the present disclosure, the portion of the first computing module that identifies the user content structure rules from the posting information is configured to:

[0037] Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters;

[0038] Recognizing preset pattern characters in the posted information;

[0039] All recognized preset pattern characters are used as user content structure rules.

[0040] In one implementation of the present disclosure, the part of the second calculation module that constructs the user posting time regularity matrix based on the posting times is configured as follows:

[0041] Count the number of posts in a preset time period;

[0042] Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

[0043] In one implementation of the present disclosure, the part of the second calculation module that constructs the user posting time regularity matrix based on the posting times is further configured to:

[0044] If there is interaction between users, a bias is added to the corresponding element in the user posting time regularity matrix.

[0045] In one implementation of the present disclosure, the apparatus further includes:

[0046] The processing module is configured to perform normalization processing on the user posting time regularity matrix.

[0047] In one implementation of the present disclosure, the building blocks include:

[0048] A construction unit is configured to represent a social network as an undirected and unweighted graph; the undirected and unweighted graph includes user nodes and edges connecting the user nodes;

[0049] A computing unit configured to calculate the weight WJR of the edge in the undirected unweighted graph ij , the formula is as follows:

[0050] WJR ij =αJ(v i ,v j )+(1-α)R(v i ,v j );

[0051] Establishment unit, configured to be based on the weight WJR ij and undirected unweighted graphs to build network undirected weighted graphs;

[0052] Among them, α is an adjustable parameter, 0<α<1, J(v i ,v j ) represents user v i With user v j The similarity of content structure rules between users, R(v i ,v j ) represents user v i With user v j The temporal regularity similarity between users.

[0053] In one implementation of the present disclosure, it further includes:

[0054] The third calculation module is configured to calculate the modularity of the community based on the community division result.

[0055] In a third aspect, an embodiment of the present disclosure provides an electronic device comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement a method as described in any one of the first aspects.

[0056] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the method as described in any one of the first aspects is implemented.

[0057] The technical effects provided by the embodiments of the present disclosure may include the following beneficial effects:

[0058] According to the technical solution provided by the embodiment of the present disclosure, the target community discovery method that integrates content structure rules and time regularities includes: obtaining the user's posting information and the number of posts; identifying the user's content structure rules from the posting information, and calculating the content structure rule similarity between users through the Jaccard similarity coefficient; constructing a user posting time regularity matrix based on the number of posts, and calculating the time regularity similarity between users through the Pearson correlation coefficient; establishing a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users; using the spectral clustering method to perform community discovery on the network undirected weighted graph to obtain community division results. In the above technical solution, by calculating the content structure rule similarity between users and the time regularity similarity between users, the potential connections and posting habits between users can be revealed, which helps to discover hidden communities and influence networks in social platforms, accurately locate target communities based on user preferences, provide valuable insights for researchers, and provide strong support for research and application in the field of community discovery.

[0059] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings.

[0061] Figure 1 A flowchart of a target community discovery method integrating content structure rules and time regularity according to an embodiment of the present disclosure is shown.

[0062] Figure 2 A flowchart of a target community discovery method integrating content structure rules and time regularities according to a specific embodiment of the present disclosure is shown.

[0063] Figure 3 A schematic diagram showing a user posting time regularity matrix according to an embodiment of the present disclosure.

[0064] Figure 4 A structural block diagram of a target community discovery device integrating content structure rules and time regularities according to an embodiment of the present disclosure is shown.

[0065] Figure 5 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0066] Figure 6 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0067] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0068] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, actions, components, parts, or combinations thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof exist or are added.

[0069] It should also be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0070] Although current community detection technology has alleviated the efficiency issues faced by community detection to a certain extent, it often focuses on the topology of the network and pays less attention to users' personalized preferences, making it difficult to accurately locate target communities based on user preferences for specific applications.

[0071] Taking the above-mentioned defects into consideration, the target community discovery method provided by the present disclosure that integrates content structure rules and time regularities includes: obtaining the user's posting information and the number of posts; identifying the user's content structure rules from the posting information, and calculating the content structure rule similarity between users through the Jaccard similarity coefficient; constructing a user posting time regularity matrix based on the number of posts, and calculating the time regularity similarity between users through the Pearson correlation coefficient; establishing a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users; using the spectral clustering method to perform community discovery on the network undirected weighted graph to obtain community division results. In the above technical solution, by calculating the content structure rule similarity between users and the time regularity similarity between users, the potential connections and posting habits between users can be revealed, which helps to discover hidden communities and influence networks in social platforms, accurately locate target communities based on user preferences, provide valuable insights for researchers, and provide strong support for research and application in the field of community discovery.

[0072] Figure 1 A flowchart of a target community discovery method integrating content structure rules and time regularity according to an embodiment of the present disclosure is shown.

[0073] like Figure 1 As shown, the target community discovery method integrating content structure rules and time regularity includes the following steps S110-S150:

[0074] In step S110, the user's posting information and posting times are obtained;

[0075] In step S120, user content structure rules are identified from the posting information, and the similarity of content structure rules between users is calculated using the Jaccard similarity coefficient;

[0076] In step S130, a user posting time regularity matrix is ​​constructed based on the number of posts, and the time regularity similarity between users is calculated using the Pearson correlation coefficient;

[0077] In step S140, a network undirected weighted graph is established based on the content structure rule similarity between users and the time regularity similarity between users;

[0078] In step S150 , a spectral clustering method is used to perform community discovery on the undirected weighted graph of the network to obtain a community division result.

[0079] In this method, we first collect the posting information of each user on the social platform, and then conduct user feature mining based on this posting information. Specifically, posting information may include posting time, posting content, and posting type (e.g., text, image, video, etc.). When conducting user feature mining, we extract content structure rules from each user's posting information to obtain each user's user content structure rules. We then calculate the similarity of content structure rules between users. In this method, we calculate the similarity of content structure rules between users using the Jaccard similarity coefficient.

[0080] In the method disclosed herein, based on the posting records of user posting information, the number of times a user posts in a preset time period, such as every hour or every two hours, can be counted, and then user feature mining can be performed based on the number of posts. Specifically, when performing user feature mining, the user posting information can be aggregated into a list according to a preset time period, such as hours, to represent the time pattern of the user in the corresponding hour, and then a user posting time pattern matrix is ​​constructed, in which rows represent individual users, columns represent different time periods, and the elements in the matrix represent the number of posts or time patterns of a user in a certain time period. Then, the time pattern similarity between users is calculated based on the user posting time pattern matrix of each user. In the present disclosure, the time pattern similarity between users is calculated by the Pearson correlation coefficient.

[0081] In the disclosed method, the similarity of content structure rules between users and the similarity of time patterns between users are weighted and merged into a comprehensive weight to construct a network undirected weighted graph, and the spectral clustering method is used to perform community discovery on the network undirected weighted graph. Users with similar posting writing habits and similar posting time periods can be divided into the same community, making the node similarity within the cluster higher and more stable, and the similar users that can be portrayed are more explanatory and persuasive, and the quality of community discovery will be better.

[0082] In one implementation of the present disclosure, the step S120 of identifying the user content structure rule from the posting information includes:

[0083] Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters;

[0084] Recognizing preset pattern characters in the posted information;

[0085] All recognized preset pattern characters are used as user content structure rules.

[0086] In the disclosed method, when performing content structure rule recognition, it is necessary to construct a pattern set for content structure rule recognition. The pattern set in the disclosed method includes preset pattern characters, which can be dates such as year, month, day, etc., numbers such as 1, 2, 3, etc., symbols such as curly brackets {}, parentheses (), etc., and special characters such as triangle ▲, circle ●, etc.

[0087] In a specific embodiment, the pattern set P = {year, month, day, release, name, http, #, \$,:, ;, ;, \+, ↓, ■, ▲, ◆, ●, ★, ☆, 〈, 〉, 《,》, 「,」, 『, 』,

,

[0088] Then, use regular expressions or AC automata to find all possible locations of the pattern set P in the text of the message, and then record all the preset pattern characters R={r1,r2,r3,…,r n}, all the recognized preset pattern characters R are used as user content structure rules. Among them, r1, r2, r3, ..., r n is a single recognized preset pattern character, and n is the number of recognized preset pattern characters.

[0089] In one implementation of the present disclosure, the Jaccard similarity coefficient (i.e., the Jaccard coefficient) is applied to set operations on word frequencies, calculating the ratio of the intersection to the union of two sets. A metric related to the Jaccard coefficient is called the Jaccard distance, which describes the dissimilarity between sets. The larger the Jaccard distance, the lower the sample similarity.

[0090] In step S120, the similarity of content structure rules between users is calculated using the Jaccard similarity coefficient. The Jaccard coefficient is defined as the ratio of the size of the intersection of the preset pattern characters in the content structure rules of user a and user b to the size of the union of the preset pattern characters in the content structure rules of user a and user b, and is defined as follows:

[0091]

[0092] Among them, R a is the preset pattern character set in the content structure rule of user a, R b is the set of preset pattern characters in the content structure rule of user b, when the set R a , R b When both are empty, J(a,b) is defined as 1.

[0093] In one implementation of the present disclosure, constructing a user posting time regularity matrix based on the posting times in step S130 includes:

[0094] Count the number of posts in a preset time period;

[0095] Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

[0096] In the disclosed method, a user's posting time regularity feature is constructed to describe the user's posting time habits. The preset time period in the disclosed method can be minutes, hours, etc. The statistics of the number of user posts can only count the user's own posts, and can also include the user's forwarding of other users' posts as the user's posting count.

[0097] The preset time period for statistics is hourly. Taking 24 hours a day as an example, a matrix of user posting time patterns is constructed. For example Figure 3 As shown, the rows represent users V1, V2, V3...Vn, the columns represent time periods 0-23, and the elements in the matrix represent the number of posts by the user in the time period.

[0098] In one implementation of the present disclosure, the step S130 of constructing a user posting time regularity matrix based on the number of posts further includes:

[0099] If there is interaction between users, a bias is added to the corresponding element in the user posting time regularity matrix.

[0100] In the disclosed method, interactive behaviors between users, such as comments and likes, indicate that there is a relationship between the users. Therefore, a bias can be added to the matrix elements corresponding to the users with interactive relationships. Subsequently, when the temporal regularity similarity between users is calculated based on the biased matrix elements and community discovery is performed, the users with interactive relationships can be divided into the same community as much as possible.

[0101] In one implementation of the present disclosure, before calculating the temporal regularity similarity between users by using the Pearson correlation coefficient in step S130, the method further includes:

[0102] The user posting time regularity matrix is ​​normalized.

[0103] In the disclosed method, since the number of posts by different users may vary greatly, the temporal regularity matrix needs to be normalized to eliminate the magnitude differences between users. Specifically, the maximum and minimum normalization method or the Z-score normalization method can be used to eliminate the magnitude differences.

[0104] In one implementation of the present disclosure, in step S130, the temporal regularity similarity between users is calculated using the Pearson correlation coefficient. The Pearson correlation coefficient is used to measure the linear correlation between two variables. The calculation formula is as follows:

[0105]

[0106] Where x i Represents different preset time period lengths; y iRepresents the time regularity score of the user's posting in each preset time period, which is set according to the number of posts by the user in the time period. For example, the more times the user posts, the higher the score. m is the number of preset time periods in the statistical duration. For example, if the preset time period is counted in hours and the statistical duration is 1 day, then m is 24.

[0107] The posting and forwarding activities of users in the same region on social platforms generally have obvious periodicity. The posting time patterns of users in different time periods within a 24-hour day vary greatly. The similarity of the time periods of heavy posting of different users every day is of reference value for calculating the similarity of time patterns between users.

[0108] In one implementation of the present disclosure, the step S140 of establishing a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users includes:

[0109] The social network is represented as an undirected unweighted graph; the undirected unweighted graph includes user nodes and edges connecting the user nodes;

[0110] Calculate the weight WJR of the edge in the undirected unweighted graph ij , the formula is as follows:

[0111] WJR ij =αJ(v i ,v j )+(1-α)R(v i ,v j );

[0112] Based on the weights WJR ij and undirected unweighted graphs to build network undirected weighted graphs;

[0113] Among them, α is an adjustable parameter, 0<α<1, J(v i ,v j ) represents user v i With user v j The similarity of content structure rules between users, R(v i ,v j ) represents user v i With user v j The temporal regularity similarity between users.

[0114] In one implementation of the present disclosure, in step S150 , a spectral clustering method is used to perform community discovery on the undirected weighted graph of the network to obtain a community division result.

[0115] In one implementation of the present disclosure, it further includes:

[0116] The modularity of the community is calculated based on the community division result.

[0117] In the present disclosure, modularity, also known as modularity metric, is a commonly used method for measuring the strength of network community structure. The size of modularity can be used to quantitatively measure the quality of network community division. The closer its value is to 1, the stronger the strength of the community structure divided by the network, that is, the better the division quality.

[0118] Figure 2 A flowchart of a target community discovery method integrating content structure rules and time regularities according to a specific embodiment of the present disclosure is shown.

[0119] like Figure 2 As shown, the target community discovery method that integrates content structure rules and time patterns includes two stages: a feature mining and comparison stage and a target community discovery stage. In the feature mining and comparison stage, each user's posting information on the social platform is collected, including posting time, content, type (such as text, picture, video), etc. On the one hand, content structure rules are extracted, and a specified pattern string is identified from each user's posting information as each user's content structure rule, and then the similarity of content structure rules between users is calculated using the Jaccard similarity coefficient. On the other hand, each user's posting information is aggregated into time periods. For example, user posting information is aggregated into a list by hour, representing the user's posting time pattern within the corresponding hour, and then the similarity of time patterns between users is measured using the Pearson correlation coefficient. In the target community discovery stage, the similarity of content structure rules and time patterns between users are combined into a comprehensive weight to construct a weighted undirected graph. Then, the target community is discovered based on the spectral clustering method (community discovery) and modularity Q (Modularity) is used as the evaluation indicator for community division results. The quality of candidate target communities is comprehensively ranked by modularity comparison, and the target communities with the highest ranking are selected.

[0120] Figure 4 A structural block diagram of a target community discovery device integrating content structure rules and time regularity according to an embodiment of the present disclosure is shown. The device can be implemented as part or all of an electronic device through software, hardware, or a combination of both.

[0121] like Figure 4 As shown, the target community discovery device 400 integrating content structure rules and time regularities includes:

[0122] The acquisition module 410 is configured to obtain the user's posting information and posting times;

[0123] A first calculation module 420 is configured to identify user content structure rules from the posting information and calculate the similarity of content structure rules between users using the Jaccard similarity coefficient;

[0124] The second calculation module 430 is configured to construct a user posting time regularity matrix based on the posting times, and calculate the time regularity similarity between users using the Pearson correlation coefficient;

[0125] A construction module 440 is configured to establish a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users;

[0126] The community discovery module 450 is configured to perform community discovery on the network undirected weighted graph using a spectral clustering method to obtain a community division result.

[0127] The target community discovery device provided by the present disclosure, which integrates content structure rules and time patterns, can reveal the potential connections and posting habits between users by calculating the similarity of content structure rules and time patterns between users. This helps to discover hidden communities and influence networks in social platforms, accurately locate target communities based on user preferences, provide valuable insights for researchers, and provide strong support for research and application in the field of community discovery.

[0128] In one implementation of the present disclosure, the portion of the first calculation module 420 that identifies the user content structure rules from the posting information is configured to:

[0129] Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters;

[0130] Recognizing preset pattern characters in the posted information;

[0131] All recognized preset pattern characters are used as user content structure rules.

[0132] In one implementation of the present disclosure, the part of the second calculation module 430 that constructs the user posting time regularity matrix based on the posting times is configured as follows:

[0133] Count the number of posts in a preset time period;

[0134] Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

[0135] In one implementation of the present disclosure, the part of the second calculation module 430 that constructs the user posting time regularity matrix based on the posting times is further configured to:

[0136] If there is interaction between users, a bias is added to the corresponding element in the user posting time regularity matrix.

[0137] In one implementation of the present disclosure, the apparatus further includes:

[0138] The processing module is configured to perform normalization processing on the user posting time regularity matrix.

[0139] In one implementation of the present disclosure, the building module 440 includes:

[0140] A construction unit is configured to represent a social network as an undirected and unweighted graph; the undirected and unweighted graph includes user nodes and edges connecting the user nodes;

[0141] A computing unit configured to calculate the weight WJR of the edge in the undirected unweighted graph ij , the formula is as follows:

[0142] WJR ij =αJ(v i ,v j )+(1-α)R(v i ,v j );

[0143] Establishment unit, configured to be based on the weight WJR ij and undirected unweighted graphs to build network undirected weighted graphs;

[0144] Among them, α is an adjustable parameter, 0<α<1, J(v i ,v j ) represents user v i With user v j The similarity of content structure rules between users, R(v i ,v j ) represents user v i With user v j The temporal regularity similarity between users.

[0145] In one implementation of the present disclosure, it further includes:

[0146] The third calculation module is configured to calculate the modularity of the community based on the community division result.

[0147] The present disclosure also discloses an electronic device, Figure 5 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0148] like Figure 5 As shown, the electronic device includes a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to an embodiment of the present disclosure.

[0149] The target community discovery method integrating content structure rules and time regularities includes:

[0150] Get the user's posting information and posting times;

[0151] Identifying user content structure rules from the posting information, and calculating the similarity of content structure rules between users using the Jaccard similarity coefficient;

[0152] Build a user posting time regularity matrix based on the number of posts, and calculate the time regularity similarity between users using the Pearson correlation coefficient;

[0153] Establishing a network undirected weighted graph based on the similarity of content structure rules between users and the similarity of time regularity between users;

[0154] A spectral clustering method is used to perform community discovery on the undirected weighted graph of the network to obtain a community division result.

[0155] In one implementation of the present disclosure, identifying the user content structure rule from the posting information includes:

[0156] Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters;

[0157] Recognizing preset pattern characters in the posted information;

[0158] All recognized preset pattern characters are used as user content structure rules.

[0159] In one implementation of the present disclosure, constructing a user posting time regularity matrix based on the posting times includes:

[0160] Count the number of posts in a preset time period;

[0161] Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

[0162] In one implementation of the present disclosure, constructing a user posting time regularity matrix based on the posting times further includes:

[0163] If there is interaction between users, a bias is added to the corresponding element in the user posting time regularity matrix.

[0164] In one implementation of the present disclosure, before calculating the temporal regularity similarity between users using the Pearson correlation coefficient, the method further includes:

[0165] The user posting time regularity matrix is ​​normalized.

[0166] In one implementation of the present disclosure, establishing a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users includes:

[0167] The social network is represented as an undirected unweighted graph; the undirected unweighted graph includes user nodes and edges connecting the user nodes;

[0168] Calculate the weight WJR of the edge in the undirected unweighted graph ij , the formula is as follows:

[0169] WJR ij =αJ(v i ,v j )+(1-α)R(v i ,v j );

[0170] Based on the weights WJR ij As well as undirected unweighted graphs to build network undirected weighted graphs;

[0171] Among them, α is an adjustable parameter, 0<α<1, J(v i ,v j ) represents user v i With user v j The similarity of content structure rules between users, R(v i ,v j ) represents user v i With user v j The temporal regularity similarity between users.

[0172] In one implementation of the present disclosure, it further includes:

[0173] The modularity of the community is calculated based on the community division result.

[0174] Figure 6 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown.

[0175] like Figure 6 As shown, the computer system includes a processing unit, which can execute the various methods in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). In the RAM, various programs and data required for the operation of the computer system are also stored. The processing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0176] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, etc.; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN card, a modem, etc. The communication part performs a communication process via a network such as the Internet. The drive is also connected to the I / O interface as needed. Removable media, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed. Among them, the processing unit can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0177] In particular, according to embodiments of the present disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for executing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication component and / or installed from a removable medium.

[0178] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0179] The units or modules involved in the embodiments described in this disclosure may be implemented by software or programmable hardware. The units or modules described may also be provided in a processor, and the names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0180] As another aspect, the present disclosure further provides a computer-readable storage medium. This computer-readable storage medium may be included in the electronic device or computer system described in the above embodiments, or may be a standalone computer-readable storage medium not incorporated into the device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the methods described in the present disclosure.

[0181] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims

1. A target community discovery method that integrates content structure rules and time regularity, characterized by: include: Get the user's posting information and posting times; Identifying user content structure rules from the posting information, and calculating the similarity of content structure rules between users using the Jaccard similarity coefficient; Build a user posting time regularity matrix based on the number of posts, and calculate the time regularity similarity between users using the Pearson correlation coefficient; The content structure rule similarity between users and the time regularity similarity between users are weighted and combined into a comprehensive weight, and an undirected weighted graph of the network based on the content structure rule similarity between users and the time regularity similarity between users is established; Using the spectral clustering method to perform community discovery on the undirected weighted graph of the network to obtain community division results; The identifying of user content structure rules from the posting information includes: Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters; Using regular expressions or AC automaton algorithms to identify preset pattern characters in the posted information; All recognized preset pattern characters are used as user content structure rules; The constructing of a user posting time regularity matrix based on the posting times includes: Count the number of posts in a preset time period; Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

2. The target community discovery method integrating content structure rules and time regularity according to claim 1 is characterized in that: The constructing of a user posting time regularity matrix based on the posting times further includes: If there is interaction between users, a bias is added to the corresponding element in the user posting time regularity matrix.

3. The target community discovery method integrating content structure rules and time regularity according to claim 2 is characterized in that: Before calculating the temporal regularity similarity between users using the Pearson correlation coefficient, the method further includes: The user posting time regularity matrix is ​​normalized.

4. The method for discovering target communities by integrating content structure rules and time regularities according to claim 1, characterized in that: The establishing of a network undirected weighted graph based on the similarity of content structure rules between users and the similarity of time regularity between users includes: The social network is represented as an undirected unweighted graph; the undirected unweighted graph includes user nodes and edges connecting the user nodes; Calculate the weight WJR of the edge in the undirected unweighted graph ij , the formula is as follows: WJR ij =αJ(v i ,v j )+(1-α)R(v i ,v j ); Based on the weights WJR ij and undirected unweighted graphs to build network undirected weighted graphs; Among them, α is an adjustable parameter, 0<α<1, J(v i ,v j ) represents user v i With user v j The similarity of content structure rules between users, R(v i ,v j ) represents user v i With user v j The temporal regularity similarity between users.

5. The target community discovery method integrating content structure rules and time regularity according to claim 1 is characterized in that: Also includes: The modularity of the community is calculated based on the community division result.

6. A target community discovery device that integrates content structure rules and time patterns, characterized in that: include: An acquisition module is configured to obtain user posting information and posting times; A first calculation module is configured to identify user content structure rules from the posting information and calculate the similarity of content structure rules between users using the Jaccard similarity coefficient; A second calculation module is configured to construct a user posting time regularity matrix based on the number of posts, and calculate the time regularity similarity between users using the Pearson correlation coefficient; A construction module is configured to weight and combine the content structure rule similarity between users and the time regularity similarity between users into a comprehensive weight, and establish a network undirected weighted graph based on the content structure rule similarity between users and the time regularity similarity between users; A community discovery module is configured to use a spectral clustering method to perform community discovery on the undirected weighted graph of the network to obtain a community division result; The identifying of user content structure rules from the posting information includes: Constructing a pattern set for content structure rule recognition; the pattern set includes preset pattern characters; Using regular expressions or AC automaton algorithms to identify preset pattern characters in the posted information; All recognized preset pattern characters are used as user content structure rules; The constructing of a user posting time regularity matrix based on the posting times includes: Count the number of posts in a preset time period; Based on the preset time period, the number of posts in the preset time period is used to construct a user posting time regularity matrix.

7. An electronic device, characterized in that: The method comprises a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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