Network information propagation capability prediction method based on node energy model

Abstract

The invention discloses a network information propagation capability prediction method based on a node energy model, and relates to the field of network information propagation. A network information dissemination capability prediction method based on a node energy model comprises the following steps: S1, acquiring network information, reading an information dissemination network, S2, judging node state attributes on the information dissemination network, S3, calculating a success rate and an infection rate of dissemination information from a first node to a second node, S4, calculating a success rate and an infection rate of dissemination of network information, and S5, predicting the dissemination capability of the network information according to the success rate and the infection rate. S5, inputting a calculation result into a prediction model; and S6, obtaining a prediction result of the network information propagation capability. The method solves the problem that in the prior art, due to the fact that the network information spreading capacity is difficult to predict, control cannot be carried out according to the predicted network information spreading result, the network information spreading condition can be known in time, the network information with risks can be blocked in time, more response time is provided for risk processing, and the network information spreading quality is improved. And the negative influence is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of network information dissemination, and in particular to a method for predicting network information dissemination capability based on a node energy model. Background Art

[0002] Measuring the ability of network information dissemination has a wide range of applications in public opinion research, information promotion, computer virus prevention and control, etc. Since people began to study complex networks, many indicators have been proposed to characterize network characteristics, such as network diameter, number of triangles, node intermediary coefficient, etc. These indicators can show the ability of the network to disseminate information from different aspects.

[0003] A Chinese patent with publication number CN110190999B discloses a method for measuring the information dissemination capability of a network based on a node energy model. The method uses an information dissemination model based on node energy to perform simulation on the network, and distinguishes the influence of nodes on the success rate and infection rate by screening nodes of different degrees. Then, the overall success rate and infection rate of the network are corrected by weighted coefficients. The method has good stability, and the experimental results on networks of the same nature and different scales are similar. Small-scale networks of the same nature can be used to evaluate large-scale networks. When measuring the information dissemination capability of the network, the method also takes into account the influence of the success rate on the information dissemination capability, and incorporates the success rate and infection rate into the evaluation indicators at the same time, which can fully and effectively measure the information dissemination capability of the network.

[0004] In the actual use of the above patent, it is difficult to predict the ability of network information propagation, and it is impossible to control the network information propagation and take corresponding countermeasures according to the predicted network information propagation results; therefore, it does not meet the existing needs. In this regard, we propose a method for predicting the network information propagation ability based on the node energy model. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting the network information propagation capacity based on a node energy model. By predicting the propagation capacity of network information in an information propagation network, it is possible to timely understand the propagation status of network information, timely block risky network information, and provide more response time for handling risks, thereby reducing the risk of information propagation and further reducing negative impacts, thereby solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for predicting network information dissemination capacity based on a node energy model, comprising the following steps:

[0007] S1: Acquire network information, establish a historical network information dissemination attribute database, and read the information dissemination network of network information dissemination.

[0008] S2: After initializing the energy and related properties of the test network nodes, the first node is set, and the state properties of the nodes on the information dissemination network are determined.

[0009] S3: Calculate the energy changes of nodes on the information dissemination network and the success rate and infection rate of the first node in disseminating information to the second node.

[0010] S4: Calculate the success rate and infection rate of the first node receiving the information transmitted from the second node, record the propagation status, and calculate the success rate and infection rate of the network information propagation in the information propagation network.

[0011] S5: The calculated success rate and infection rate as well as the node status attributes on the information propagation network are input into the prediction model.

[0012] S6: Predict the propagation capability of network information through the prediction model to obtain the prediction result of the propagation capability of network information.

[0013] Preferably, the obtaining of network information and establishing a historical network information propagation attribute database specifically includes:

[0014] The network information is obtained, one of the information publishers is selected as the initial propagation node, and the information propagation network is constructed with the direct forwarding nodes of the initial propagation node and the direct forwarding directed relationship.

[0015] Then, corresponding propagation networks are constructed for the multiple historical network information with historical information credibility and the corresponding network information.

[0016] The properties of each propagation node and propagation topology structure in the constructed information propagation network are extracted, and then the credibility of each historical network information is marked to establish a historical network information propagation attribute database.

[0017] Preferably, selecting one of the information publishers as the initial propagation node includes:

[0018] Extracting information publishing data of each information publisher, wherein the information publishing data includes information publishing time interval, number of information published per unit time, and number of forwarded information;

[0019] The information release data is used to obtain the information release evaluation parameters corresponding to each information publisher; wherein the information release evaluation parameters are obtained by the following formula:

[0020] P=(1+Xr+Yr)·P 0

[0021]

[0022] Where P represents the information release evaluation parameter; P0 Indicates the preset parameter reference value; X r and Y r represents the first coefficient and the second coefficient; n represents the number of unit time experienced by the information publisher; X i represents the number of information released in the i-th unit time; T p represents the average time interval between information releases; T max Indicates the maximum time interval for information release; Z max Indicates the maximum number of forwardings of information release;

[0023] Sorting the information release evaluation parameters in descending order to obtain an information release evaluation parameter sequence;

[0024] Retrieve the preset number M of candidate propagation points;

[0025] According to the candidate number M of the propagation points, M information release evaluation parameters are selected from large to small in the information release evaluation parameter sequence, and information publishers corresponding to the M information release evaluation parameters are used as candidate information publishers;

[0026] Retrieving the information release time of each candidate information publisher;

[0027] Using the information release time in combination with the information release evaluation parameter of each candidate information publisher, obtaining the information release evaluation value corresponding to each candidate information publisher;

[0028] The candidate information publisher with the highest information publishing evaluation value is used as the initial propagation node.

[0029] Preferably, the information release evaluation value corresponding to each candidate information publisher is obtained by using the information release time in combination with the information release evaluation parameter of each candidate information publisher, including:

[0030] The information release compensation coefficient corresponding to each candidate information publisher is obtained by using each information release time in the historical information release record corresponding to each candidate information publisher, wherein the information release compensation coefficient is obtained by the following formula:

[0031]

[0032] Where S represents the information release compensation coefficient corresponding to each candidate information publisher; k represents the number of first information releases in the historical information release records corresponding to each candidate information publisher; C represents the total number of information releases corresponding to each candidate information publisher; T i represents the time when the i-th information is released as the first information; T xiIt indicates the time when the second publisher of information publishes information for the i-th time when the information is first published;

[0033] The information release compensation coefficient is combined with the information release evaluation parameter of each candidate information publisher to obtain the information release evaluation value corresponding to each candidate information publisher, wherein the information release evaluation value is obtained by the following formula:

[0034]

[0035] Wherein, H represents the information release evaluation value corresponding to each candidate information publisher.

[0036] Preferably, the calculation of the success rate and infection rate of network information propagation in the information propagation network specifically includes:

[0037] According to the node degree on the information propagation network from small to large, the node sequence of the information propagation network is counted and the attributes of the nodes are initialized.

[0038] The weighted success rate and infection rate can be obtained by multiplying the success rate and infection rate of each node degree by the weight of the node degree in the total number of nodes.

[0039] Preferably, the predicting the propagation capability of network information by using a prediction model specifically includes:

[0040] A propagation attribute between a first node and a second node in a preset information propagation network is preset, and a historical duration of propagation of historical network information corresponding to the preset propagation attribute from the first node to the second node is determined as a preset duration.

[0041] According to the preset propagation property between the first node and the second node in the information propagation network, and the preset time length for the network information to be predicted to propagate from the first node to the second node, the first propagation success rate and infection rate of the network information to be predicted to propagate from the first node to the second node are determined.

[0042] According to the first propagation success rate and infection rate of the information propagation network to be predicted from each node connected to the second node to the second node, the second propagation success rate and infection rate of the network information to be predicted to be propagated to the second node are determined.

[0043] According to the second propagation success rates and infection rates of multiple nodes, a propagation capability prediction result of the network information to be predicted in the information propagation network is calculated.

[0044] Preferably, the prediction model specifically includes:

[0045] A determination module is used to determine the historical propagation success rate and infection rate of historical information and preset propagation parameters and preset duration according to the historical propagation results of historical information in the information propagation network, and calculate the first propagation success rate and infection rate and the second propagation success rate and infection rate of network information propagated from the first node to the second node.

[0046] The prediction module calculates the propagation capacity prediction result of the network information to be predicted in the information propagation network according to the number of nodes to which the network information to be predicted is propagated.

[0047] Preferably, the determination module specifically includes:

[0048] The history determination module is used to determine the historical propagation success rate and infection rate of the historical information and the preset propagation parameters and the preset duration according to the historical propagation results of the historical information in the information propagation network.

[0049] The calculation module is used to calculate the first propagation success rate and infection rate of the network information to be predicted according to the preset propagation parameters and the preset time, and calculate the second propagation success rate and infection rate of the network information to be predicted according to the first propagation success rate and infection rate.

[0050] Preferably, the calculation module specifically includes:

[0051] The first calculation module is used to calculate the first propagation success rate and infection rate of the network information to be predicted from the first node to the second node according to the preset propagation parameters and the preset time between the first node and the second node in the information propagation network.

[0052] The second calculation module is used to calculate the second propagation success rate and infection rate of the network information to be predicted from each node connected to the second node to the second node according to the first propagation success rate and infection rate of the network information to be predicted from each node connected to the second node to the second node.

[0053] Preferably, the history determination module specifically includes:

[0054] The historical propagation module is used to determine the historical propagation success rate and infection rate of the historical information between the first node and the second node according to the historical propagation results of the historical information in the information propagation network.

[0055] The parameter and duration determination module is used to determine the preset propagation parameters and preset duration between the first node and the second node according to the historical propagation success rate and infection rate between the first node and the second node of a plurality of historical information.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] By predicting the propagation capacity of network information in the information dissemination network, the present invention can timely understand the propagation status of network information, timely block risky network information, provide more response time for handling risks, thereby reducing information dissemination risks, and further reducing negative impacts, and improving the reliability and accuracy of predictions of network information propagation in the information dissemination network. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the flow of the method for predicting network information dissemination capability based on the node energy model of the present invention;

[0059] Figure 2 It is a schematic diagram of a module of a method for predicting network information dissemination capability based on a node energy model of the present invention;

[0060] Figure 3 It is a prediction diagram of the prediction model of the network information propagation capability prediction method based on the node energy model of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In order to solve the problem that the existing technology is difficult to predict the ability of network information dissemination in actual use, it is impossible to control the network information dissemination and take corresponding countermeasures according to the predicted network information dissemination results, please refer to Figure 1-Figure 3 , this embodiment provides the following technical solutions:

[0063] The method for predicting network information dissemination capability based on a node energy model comprises the following steps:

[0064] S1: Acquire network information, establish a historical network information dissemination attribute database, and read the information dissemination network of network information dissemination.

[0065] Specifically, the entire established influence propagation network is divided into two parts, one is the training set and the other is the prediction set. The training set is then used to calculate the success rate and infection rate of the edges in the prediction set. For each calculation, an existing network information and a non-existent network information are randomly selected in the prediction set. The scores of these two network information under the prediction algorithm are calculated according to the training set. If the existing network information has a high score, it is recorded as 1, and the non-existent network information has a high score, which is recorded as 0. If they are equal, they are recorded as 0.5. This calculation is performed multiple times, and then all 1s and 0.5s are added together and divided by the number of times to get the final calculation result. If the final calculation result is greater than 0.5, it means that the prediction algorithm effectively predicts the propagation ability of network information in the information propagation network.

[0066] S2: After initializing the energy and related properties of the test network nodes, the first node is set, and the state properties of the nodes on the information dissemination network are determined. The state properties of the nodes on the information dissemination network are ring paths and chain paths.

[0067] Specifically, according to the path information in the basic data of the information dissemination network, any node in the basic data of the information dissemination network is taken as the starting point, that is, the first node, a starting path is separately constructed for the starting point and all the relationship nodes having a connection relationship with it, and a starting sequence identifier is assigned to the starting node in each starting path, and according to the path information in the first node data, the next hop nodes having a connection relationship with the relationship nodes in each starting path are continuously obtained as intermediate nodes, and all the obtained intermediate nodes are assigned sequence identifiers according to the order in which they are obtained and the starting path they are located in. When the next hop node cannot be obtained or the obtained next hop node is a node in the starting path, the intermediate node at the end of the node sequence identifier of the starting path is determined to be the termination node, and the starting path whose termination node is a node in the starting path is determined to be a ring path, and the remaining starting paths are chain paths.

[0068] S3: Calculate the energy changes of nodes on the information dissemination network and the success rate and infection rate of the first node in disseminating information to the second node.

[0069] S4: Calculate the success rate and infection rate of the first node receiving the information transmitted from the second node, record the propagation status, and calculate the success rate and infection rate of the network information propagation in the information propagation network.

[0070] Calculate the success rate and infection rate of network information in the information dissemination network, including:

[0071] According to the node degree on the information propagation network from small to large, the node sequence of the information propagation network is counted and the attributes of the nodes are initialized.

[0072] The weighted success rate and infection rate can be obtained by multiplying the success rate and infection rate of each node degree by the weight of the node degree in the total number of nodes.

[0073] Specifically, by calculating the success rate and infection rate of the first node receiving information from the second node and recording the propagation status of the network information, the success rate and infection rate of the network information in the information propagation network can be accurately calculated, providing a strong basis for predicting the propagation capacity of network information.

[0074] S5: The calculated success rate and infection rate as well as the node status attributes on the information propagation network are input into the prediction model.

[0075] Prediction models, including:

[0076] A determination module is used to determine the historical propagation success rate and infection rate of historical information and preset propagation parameters and preset duration according to the historical propagation results of historical information in the information propagation network, and calculate the first propagation success rate and infection rate and the second propagation success rate and infection rate of network information propagated from the first node to the second node.

[0077] The prediction module calculates the propagation capacity prediction result of the network information to be predicted in the information propagation network according to the number of nodes to which the network information to be predicted is propagated.

[0078] Determine the modules, including:

[0079] The history determination module is used to determine the historical propagation success rate and infection rate of the historical information and the preset propagation parameters and the preset duration according to the historical propagation results of the historical information in the information propagation network.

[0080] The calculation module is used to calculate the first propagation success rate and infection rate of the network information to be predicted according to the preset propagation parameters and the preset time, and calculate the second propagation success rate and infection rate of the network information to be predicted according to the first propagation success rate and infection rate.

[0081] Computing module, including:

[0082] The first calculation module is used to calculate the first propagation success rate and infection rate of the network information to be predicted from the first node to the second node according to the preset propagation parameters and the preset time between the first node and the second node in the information propagation network.

[0083] The second calculation module is used to calculate the second propagation success rate and infection rate of the network information to be predicted from each node connected to the second node to the second node according to the first propagation success rate and infection rate of the network information to be predicted from each node connected to the second node to the second node.

[0084] History determination module, specifically including:

[0085] A historical propagation module is used to determine the historical propagation success rate and infection rate of historical information between a first node and a second node based on the historical propagation results of historical information in the information propagation network. The historical propagation results include reference nodes among multiple nodes that receive historical information and the time when each reference node receives the historical information.

[0086] The parameter and duration determination module is used to determine the preset propagation parameters and preset duration between the first node and the second node based on the historical propagation success rate and infection rate of multiple historical information between the first node and the second node. It can effectively predict the propagation influence under the interaction of different information, and can make a certain degree of prediction for the propagation of network information. Using the prediction algorithm, the influence propagation capacity value is obtained.

[0087] Specifically, by determining the historical success rate and infection rate of information dissemination networks, and predicting the ability of network information to spread in information dissemination networks, it is possible to timely understand the dissemination of network information, promptly block risky network information, and avoid the unpredictable ability to spread network information, so that risky network information will spread widely, providing more response time for dealing with risks, thereby reducing information dissemination risks, and then reducing negative impacts, and then being able to improve the reliability and accuracy of predictions of network information dissemination in information dissemination networks.

[0088] S6: Predict the propagation capability of network information through the prediction model to obtain the prediction result of the propagation capability of network information.

[0089] Specifically, the network information and the network information propagation network are used to mine the node state attributes on the network information propagation network. According to the calculated success rate and infection rate and the node state attributes on the information propagation network, the propagation capacity of the network information in the information propagation network is obtained by weighted calculation. The calculation formula is as follows:

[0090] P A =∑k∈U S ×U R

[0091] Among them, P A represents the ability of network information to spread on the information dissemination network, k represents the predicted network information, and P A The larger the value is, the greater the ability of network information to spread on the information dissemination network is; U is the set of neighboring nodes that influence the network information, U S represents the success rate of influencing the information propagation network in the neighbor node set, U R represents the infection rate of the information propagation network affecting the set of neighbor nodes.

[0092] Obtain network information and establish a historical network information dissemination attribute database, including:

[0093] The network information is obtained, one of the information publishers is selected as the initial propagation node, and the information propagation network is constructed with the direct forwarding nodes of the initial propagation node and the direct forwarding directed relationship.

[0094] Then, corresponding propagation networks are constructed for the multiple historical network information with historical information credibility and the corresponding network information.

[0095] The properties of each propagation node and propagation topology structure in the constructed information propagation network are extracted, and then the credibility of each historical network information is marked to establish a historical network information propagation attribute database.

[0096] Specifically, one of the information publishers is selected as the initial propagation node, including:

[0097] Extracting information publishing data of each information publisher, wherein the information publishing data includes information publishing time interval, number of information published per unit time, and number of forwarded information;

[0098] The information release data is used to obtain the information release evaluation parameters corresponding to each information publisher; wherein the information release evaluation parameters are obtained by the following formula:

[0099] P=(1+Xr+Yr)·P0

[0100]

[0101] Where P represents the information release evaluation parameter; P 0 Indicates the preset parameter reference value; X r and Y r represents the first coefficient and the second coefficient; n represents the number of unit time experienced by the information publisher; X i represents the number of information released in the i-th unit time; T p represents the average time interval between information releases; T max Indicates the maximum time interval for information release; Z max Indicates the maximum number of forwardings of information release;

[0102] Sorting the information release evaluation parameters in descending order to obtain an information release evaluation parameter sequence;

[0103] Retrieve the preset number M of candidate propagation points;

[0104] According to the candidate number M of the propagation points, M information release evaluation parameters are selected from large to small in the information release evaluation parameter sequence, and information publishers corresponding to the M information release evaluation parameters are used as candidate information publishers;

[0105] Retrieving the information release time of each candidate information publisher;

[0106] Using the information release time in combination with the information release evaluation parameter of each candidate information publisher, obtaining the information release evaluation value corresponding to each candidate information publisher;

[0107] The candidate information publisher with the highest information publishing evaluation value is used as the initial propagation node.

[0108] The technical effect of the above technical solution is: by analyzing the information release data of the information publisher, extracting key indicators including information release time interval, number of information releases per unit time and forwarding amount of released information, the influence of the information publisher in information dissemination can be accurately evaluated. The information release evaluation parameters calculated by the formula can be used to sort the information publishers from large to small to form a sequence of information release evaluation parameters. According to the preset number of candidate dissemination points M, the information publishers corresponding to the first M evaluation parameters are selected from the sequence as candidate information publishers, ensuring the quality and quantity of the selected dissemination nodes. Combined with the information release time and evaluation parameters of the candidate information publishers, the information release evaluation value of each candidate information publisher is calculated, and the information publisher with the highest evaluation value is selected as the initial dissemination node. This method can ensure that the initial dissemination node plays the greatest role in the information dissemination process and improve the effect and efficiency of information dissemination. Through the above technical solution of this embodiment, the initial dissemination node can be selected more scientifically and accurately, thereby optimizing the entire information dissemination strategy. This not only helps to improve the speed and breadth of information dissemination, but also improves the accuracy and effectiveness of information dissemination, bringing better dissemination effects to information disseminators.

[0109] In summary, this technical solution optimizes the information dissemination strategy and improves the effectiveness and efficiency of information dissemination by scientifically and accurately evaluating and selecting information publishers.

[0110] Specifically, the information release time is combined with the information release evaluation parameter of each candidate information publisher to obtain the information release evaluation value corresponding to each candidate information publisher, including:

[0111] The information release compensation coefficient corresponding to each candidate information publisher is obtained by using each information release time in the historical information release record corresponding to each candidate information publisher, wherein the information release compensation coefficient is obtained by the following formula:

[0112]

[0113] Where S represents the information release compensation coefficient corresponding to each candidate information publisher; k represents the number of first information releases in the historical information release records corresponding to each candidate information publisher; C represents the total number of information releases corresponding to each candidate information publisher; T i represents the time when the i-th information is released as the first information; T xi It indicates the time when the second publisher of information publishes information for the i-th time when the information is first published;

[0114] The information release compensation coefficient is combined with the information release evaluation parameter of each candidate information publisher to obtain the information release evaluation value corresponding to each candidate information publisher, wherein the information release evaluation value is obtained by the following formula:

[0115]

[0116] Wherein, H represents the information release evaluation value corresponding to each candidate information publisher.

[0117] The technical effect of the above technical solution is: the technical solution further refines the evaluation of candidate information publishers by introducing the information release compensation coefficient S. The information release compensation coefficient takes into account the number of times each candidate information publisher is the first to release information and the number of overall information releases in the historical information release records, as well as the time difference with other information publishers. This helps to more comprehensively reflect the actual influence and contribution of the information publisher in the information dissemination process. The calculation of the information release evaluation value H not only takes into account the information release evaluation parameter P, but also combines the information release compensation coefficient S. In this way, the evaluation value takes into account the overall performance of the information publisher (such as the number of information releases, time intervals, and forwarding volume), as well as its performance at a specific moment (such as being the first publisher). This comprehensive evaluation method makes the results more accurate and comprehensive. At the same time, by introducing the information release compensation coefficient and comprehensively considering multiple factors, the technical solution can more accurately evaluate the actual influence and contribution of each candidate information publisher, thereby more scientifically selecting the initial dissemination node. This helps to improve the effect and efficiency of information dissemination, so that information can be disseminated faster and wider. Moreover, comprehensively considering multiple factors and optimizing the selection of initial dissemination nodes can help improve the effect and influence of information dissemination. This can not only increase the exposure and dissemination range of information, but also improve the credibility and acceptance of information, thereby helping information disseminators achieve better dissemination effects.

[0118] In summary, this technical solution optimizes the selection process of the initial propagation node and improves the effect and efficiency of information dissemination by introducing the information release compensation coefficient and comprehensively considering multiple factors.

[0119] The prediction model is used to predict the dissemination capacity of network information, including:

[0120] A propagation attribute between a first node and a second node in a preset information propagation network is preset, and a historical duration of propagation of historical network information corresponding to the preset propagation attribute from the first node to the second node is determined as a preset duration.

[0121] According to a preset propagation property between a first node and a second node in an information propagation network, and a preset time length for the network information to be predicted to propagate from the first node to the second node, a first propagation success rate and an infection rate of the network information to be predicted to propagate from the first node to the second node are determined. The information propagation network includes a plurality of nodes and connections between the nodes. The first node and the second node are any two nodes connected to each other among the plurality of nodes.

[0122] According to the first propagation success rate and infection rate of the information propagation network to be predicted from each node connected to the second node to the second node, the second propagation success rate and infection rate of the network information to be predicted to be propagated to the second node are determined.

[0123] According to the second propagation success rates and infection rates of multiple nodes, a propagation capability prediction result of the network information to be predicted in the information propagation network is calculated.

[0124] In summary, the network information propagation capacity prediction method based on the node energy model of the present invention, by predicting the propagation capacity of network information in the information propagation network, can timely understand the propagation status of network information, timely block risky network information, avoid the unpredictable network information propagation capacity, so that risky network information is widely spread and causes greater negative impact. At the same time, according to the propagation capacity of network information, risky information is timely blocked, providing more response time for handling risks, thereby reducing information propagation risks, and then reducing negative impacts, and then can improve the reliability and accuracy of network information propagation prediction in the information propagation network.

[0125] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0126] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting network information dissemination capability based on a node energy model, characterized in that: The following steps are involved: S1: Obtain network information, establish a historical network information dissemination attribute database, and read the information dissemination network of network information dissemination; S2: After initializing the energy and related properties of the test network nodes, set the first node and determine the state properties of the nodes on the information dissemination network; S3: Calculate the energy change of nodes on the information dissemination network and the success rate and infection rate of the first node in disseminating information to the second node; S4: Calculate the success rate and infection rate of the first node receiving the information from the second node, record the propagation status, and calculate the success rate and infection rate of the network information propagation in the information propagation network; S5: input the calculated success rate and infection rate as well as the node status attributes on the information dissemination network into the prediction model; S6: Predict the propagation capability of network information through the prediction model to obtain the prediction result of the propagation capability of network information.

2. The method for predicting network information dissemination capacity based on node energy model according to claim 1 is characterized in that: The acquisition of network information and establishment of a historical network information propagation attribute database specifically includes: Obtain network information, select one of the information publishers as the initial propagation node, and construct the information propagation network with the direct forwarding nodes and the direct forwarding directed relationship of the initial propagation node; Then, corresponding propagation networks are constructed for the multiple historical network information with historical information credibility and the corresponding network information; The properties of each propagation node and propagation topology structure in the constructed information propagation network are extracted, and then the credibility of each historical network information is marked to establish a historical network information propagation attribute database.

3. The method for predicting network information dissemination capacity based on node energy model according to claim 2 is characterized in that: Select one of the information publishers as the initial dissemination node, including: Extracting information publishing data of each information publisher, wherein the information publishing data includes information publishing time interval, number of information published per unit time, and number of forwarded information; The information release data is used to obtain the information release evaluation parameters corresponding to each information publisher; wherein the information release evaluation parameters are obtained by the following formula: P=(1+Xr+Yr)·P0 Where P represents the information release evaluation parameter; P0 represents the preset parameter benchmark value; X r and Y r represents the first coefficient and the second coefficient; n represents the number of unit time experienced by the information publisher; X i represents the number of information released in the i-th unit time; T p represents the average time interval between information releases; T max Indicates the maximum time interval for information release; Z max Indicates the maximum number of forwardings of information release; Sorting the information release evaluation parameters in descending order to obtain an information release evaluation parameter sequence; Retrieve the preset number M of candidate propagation points; According to the candidate number M of the propagation points, M information release evaluation parameters are selected from large to small in the information release evaluation parameter sequence, and information publishers corresponding to the M information release evaluation parameters are used as candidate information publishers; Retrieving the information release time of each candidate information publisher; Using the information release time in combination with the information release evaluation parameter of each candidate information publisher, obtaining the information release evaluation value corresponding to each candidate information publisher; The candidate information publisher with the highest information publishing evaluation value is used as the initial propagation node.

4. The method for predicting network information dissemination capacity based on node energy model according to claim 3 is characterized by: The information release time is combined with the information release evaluation parameter of each candidate information publisher to obtain the information release evaluation value corresponding to each candidate information publisher, including: The information release compensation coefficient corresponding to each candidate information publisher is obtained by using each information release time in the historical information release record corresponding to each candidate information publisher, wherein the information release compensation coefficient is obtained by the following formula: Where S represents the information release compensation coefficient corresponding to each candidate information publisher; k represents the number of first information releases in the historical information release records corresponding to each candidate information publisher; C represents the total number of information releases corresponding to each candidate information publisher; T i represents the time when the i-th information is released as the first information released; T xi It indicates the time when the second publisher of information publishes information for the i-th time when the information is first published; The information release compensation coefficient is combined with the information release evaluation parameter of each candidate information publisher to obtain the information release evaluation value corresponding to each candidate information publisher, wherein the information release evaluation value is obtained by the following formula: Wherein, H represents the information release evaluation value corresponding to each candidate information publisher.

5. The method for predicting network information dissemination capacity based on node energy model according to claim 1, characterized in that: The calculation of the success rate and infection rate of network information propagation in the information dissemination network specifically includes: According to the node degree on the information propagation network from small to large, the node sequence of the information propagation network is counted and the attributes of the nodes are initialized; The weighted success rate and infection rate can be obtained by multiplying the success rate and infection rate of each node degree by the weight of the node degree in the total number of nodes.

6. The method for predicting network information dissemination capacity based on node energy model according to claim 1, characterized in that: The prediction model is used to predict the dissemination capacity of network information, including: Preset a propagation attribute between a first node and a second node in the information propagation network, and determine a historical duration of propagation of historical network information corresponding to the preset propagation attribute from the first node to the second node as a preset duration; Determine a first propagation success rate and an infection rate of the network information to be predicted from the first node to the second node according to a preset propagation attribute between the first node and the second node in the information propagation network and a preset time duration for the network information to be predicted to propagate from the first node to the second node; Determine a second propagation success rate and infection rate of the network information to be predicted to be propagated to the second node according to a first propagation success rate and infection rate of the information propagation network to be predicted from each node connected to the second node to the second node; According to the second propagation success rates and infection rates of multiple nodes, a propagation capability prediction result of the network information to be predicted in the information propagation network is calculated.

7. The method for predicting network information dissemination capacity based on node energy model according to claim 1, characterized in that: The prediction model specifically includes: A determination module, used to determine the historical propagation success rate and infection rate of the historical information and preset propagation parameters and preset duration according to the historical propagation results of the historical information in the information propagation network, and calculate the first propagation success rate and infection rate and the second propagation success rate and infection rate of the network information propagated from the first node to the second node; The prediction module calculates the propagation capacity prediction result of the network information to be predicted in the information propagation network according to the number of nodes to which the network information to be predicted is propagated.

8. The method for predicting network information dissemination capacity based on node energy model according to claim 7, characterized in that: The determination module specifically includes: A history determination module, used to determine the historical propagation success rate and infection rate of the historical information and the preset propagation parameters and the preset duration according to the historical propagation results of the historical information in the information propagation network; The calculation module is used to calculate the first propagation success rate and infection rate of the network information to be predicted according to the preset propagation parameters and the preset time, and calculate the second propagation success rate and infection rate of the network information to be predicted according to the first propagation success rate and infection rate.

9. The method for predicting network information dissemination capacity based on node energy model according to claim 8, characterized in that: The computing module specifically includes: A first calculation module, used for calculating a first propagation success rate and an infection rate of the network information to be predicted from the first node to the second node according to a preset propagation parameter and a preset duration between the first node and the second node in the information propagation network; The second calculation module is used to calculate the second propagation success rate and infection rate of the network information to be predicted from each node connected to the second node to the second node according to the first propagation success rate and infection rate of the network information to be predicted from each node connected to the second node to the second node.

10. The method for predicting network information dissemination capability based on node energy model according to claim 8, characterized in that: The history determination module specifically includes: A historical propagation module, used to determine a historical propagation success rate and an infection rate of the historical information between the first node and the second node according to the historical propagation results of the historical information in the information propagation network; The parameter and duration determination module is used to determine the preset propagation parameters and preset duration between the first node and the second node according to the historical propagation success rate and infection rate between the first node and the second node of a plurality of historical information.

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