CN116150507B - Water army group identification method, device, equipment and medium - Google Patents

Water army group identification method, device, equipment and medium Download PDF

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CN116150507B
CN116150507B CN202310349637.0A CN202310349637A CN116150507B CN 116150507 B CN116150507 B CN 116150507B CN 202310349637 A CN202310349637 A CN 202310349637A CN 116150507 B CN116150507 B CN 116150507B
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network
node
cooperative
nodes
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CN116150507A (en
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罗显豪
耿雪芹
桂迎
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Hunan Eefung Software Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

Abstract

The invention provides a water army group identification method, a device, equipment and a medium, wherein the method comprises the following steps: step one: based on a social network event, analyzing whether every two pairs of users forward the same article source within a very short time interval, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the pairs of users, extracting the cooperative forwarding relationship of all the pairs of users in the social network event, and constructing a global cooperative relationship network; step two: based on a global cooperative relation network, carrying out weighted fusion on the first-order direct adjacent similarity and the neighborhood similarity to obtain node comprehensive similarity; step three: based on the comprehensive similarity of the nodes, the collaborative relationship network is subjected to group division by using a hierarchical division method, and each water army group participating in the social network event is obtained. The method can accurately mine the water army group which performs cooperative forwarding and deliberately amplifies the topic influence in the social network event, and the organization behavior and the combat mode of the water army group are examined.

Description

Water army group identification method, device, equipment and medium
Technical Field
The invention relates to the technical field of computer data processing, in particular to a water army group identification method, a water army group identification device, computer equipment and a computer readable storage medium based on social network cooperative forwarding behaviors.
Background
Under the background of rapid popularization and development of internet science and technology, netizens can freely conduct social behaviors and propagate personal views on a social platform. Due to multiparty participation of topic discussion, concealment of virtual roles and the like, various social platforms emerge a large amount of repeated information and continuously harassment our vision, and then a large amount of Internet water army combat groups correspondingly appear. The water army combat is increasingly scaled, water army groups accumulate public opinion, and false messages are transmitted, so that network safety is threatened, and social stability is affected. Therefore, the network water army group is found and monitored, and the method has important value for maintaining network safety and guaranteeing the authenticity of network information.
The technical problems to be solved in the current water army identification are as follows:
1. the traditional water army recognition method focuses on manually constructing features, and a probability value is output to recognize whether a user is a water army or not based on the user features by using a machine learning method or a deep learning method, but the accuracy of recognition is seriously dependent on screening and extraction of the user features.
2. The water army recognition method mainly comprises the steps of recognizing single users, is difficult to automatically and insignificantly observe the association relation among water armies, and cannot effectively mine water army groups and analyze the organization characteristics and the behavior patterns of the groups.
3. After the global association relation between the water armies is found and constructed, how to effectively calculate the similarity between the water armies, and divide the water armies into different groups.
Disclosure of Invention
In view of the above, the invention provides a water army group identification method, a device, a computer device and a computer readable storage medium based on social network cooperative forwarding behavior, so as to accurately mine the water army group which performs cooperative forwarding in a social network event and deliberately amplifies the topic influence, and to provide important support for monitoring and limiting water army scale operations by observing the organization behavior and the operational mode of the water army group.
The technical scheme of the invention is as follows:
in a first aspect, the invention provides a water army group identification method based on social network cooperative forwarding behavior, which comprises the following steps:
step one: based on a social network event, analyzing whether every two pairs of users forward the same article source within a very short time interval, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the pairs of users, extracting the cooperative forwarding relationship of all the pairs of users in the social network event, and constructing a global cooperative relationship network;
step two: based on a global cooperative relation network, carrying out weighted fusion on the first-order direct adjacent similarity and the neighborhood similarity to obtain node comprehensive similarity;
step three: based on the comprehensive similarity of the nodes, the collaborative relationship network is subjected to group division by using a hierarchical division method, and each water army group participating in the social network event is obtained.
In a second aspect, the invention also provides a water army group identification device based on social network cooperative forwarding behavior, which comprises the following modules:
a cooperative relationship network module: the method comprises the steps that based on a social network event, whether every two pairs of users forward the same article source in a very short time interval is analyzed, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the pairs of users, the cooperative forwarding relationship of all the pairs of users in the social network event is extracted, and a global cooperative relationship network is constructed;
and the comprehensive similarity calculation module is used for: the method comprises the steps of carrying out weighted fusion on first-order direct adjacent similarity and neighborhood similarity based on a global cooperative relation network to obtain node comprehensive similarity;
the group dividing module: the method is configured to divide groups of the collaborative relationship network by using a hierarchical division method based on the comprehensive similarity of the nodes to obtain all water army groups participating in the social network event.
In a third aspect, the present invention also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
Compared with the prior art, the invention has the beneficial effects that:
1. the method adopts the social behavior mode of cooperative forwarding to identify the water army, automatically judges abnormal behaviors, and is more accurate than a method for manually constructing the characteristic to identify the water army. The implementation process of the invention is fully automatic, the forwarding data of the network event is input, the large-scale cooperative forwarding behavior in the network event can be automatically identified, the water army group is excavated, the water army leader is identified, the speed is high, the accuracy is high, and the practical application value is high in the implementation process;
2. the invention uses the novel angle of the water army group excavation to identify the water army, and the method is more accurate than the method for identifying the single water army, and the result has more explanation significance. The water army group is excavated, and meanwhile, the water army organization characteristics and the behavior patterns can be found;
3. the invention provides a novel node comprehensive similarity method, which carries out weighted fusion on first-order direct adjacent similarity and neighborhood similarity of node pairs, and fully mines and utilizes network structure information. The global cooperative relationship network is subjected to group division based on the node comprehensive similarity, so that all water army groups participating in the same network event can be accurately mined.
The preferred embodiments of the present invention and their advantageous effects will be described in further detail with reference to specific embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate the invention and together with the description serve to explain the invention. In the drawings of which there are shown,
FIG. 1 is a flow chart of a water army group identification method based on social network cooperative forwarding behavior of the present invention;
FIG. 2 is a schematic diagram of a structure for constructing a global cooperative relationship network;
FIG. 3 is a water army group identification algorithm description diagram based on social network cooperative forwarding behavior;
FIG. 4 is a block diagram of a water army group identification device based on social network cooperative forwarding behavior.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
The water army group identification method based on the social network cooperative forwarding behavior can be applied to computer equipment such as terminals and servers. The terminal may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, which may be head-mounted devices, etc.; the server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers.
Referring to fig. 1, the invention provides a water army group identification method based on social network cooperative forwarding behavior, which comprises the following steps:
step one: based on the social network event, whether the same article source is forwarded by every two user pairs within a very short time interval is analyzed, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the user pairs, the cooperative forwarding relationship of all the user pairs in the social network event is extracted, and a global cooperative relationship network is constructed.
The cooperative forwarding behavior of the water army is defined as that every two users forward the same article source in a shorter time interval, and the occurrence frequency of the behavior reaches a preset threshold value. The cooperative forwarding behavior and the normal forwarding behavior have the following three-point abnormal modes: 1) It is normal for two users to forward the same article, but not normal at the same time; 2) Every two users occasionally forward normally at the same time, but often forward abnormally at the same time; 3) The manual operation requires a certain time, and the operation with small time difference is often the program water army behavior. The water army group is driven by benefit factors, and the method has the characteristics of cooperativity, group property, scale and the like by densely forwarding the appointed content with subjective tendency, instantly forming homogenized sound, deliberately guiding topics, manipulating the wind direction of public opinion.
The first step comprises the following steps:
s11: and calculating the cooperative forwarding relation of the user pair. For the social network event to be researched, setting a keyword group and a time range of the social network event, based on the keyword group and the time range, performing data retrieval and matching by using an API and a crawler tool to obtain social network event related data, wherein the acquired fields comprise
Figure SMS_11
(user name),>
Figure SMS_12
(user's hair->
Figure SMS_13
）、/>
Figure SMS_16
(forwarding source user name),)>
Figure SMS_17
(forwarding source article->
Figure SMS_18
）、/>
Figure SMS_19
(time of text). After the data is cleaned and preprocessed, the same article source is forwarded->
Figure SMS_1
Put the data of the same group, i.e. the transfer source article of each group of data +.>
Figure SMS_2
The values are equal. For each set of data, according to +.>
Figure SMS_3
Ascending order is performed if the user +>
Figure SMS_5
And->
Figure SMS_6
Is>
Figure SMS_7
Seconds, e.g. forwarding time interval +.>
Figure SMS_9
Second, indicate that the user is about to forward the same article source in a very short time interval, at user +.>
Figure SMS_10
And->
Figure SMS_4
And a continuous edge is constructed between the two. When in useHouse->
Figure SMS_8
And->
Figure SMS_14
When the condition of multiple cooperative forwarding occurs, the cooperative times are accumulated, and the accumulated cooperative forwarding times are the link weight of the network +.>
Figure SMS_15
。
S21: deleting the continuous edge with the continuous edge weight smaller than the set threshold value, and constructing a global cooperative relation network. If the user
Figure SMS_22
And->
Figure SMS_24
The border weight between->
Figure SMS_25
Less, possibly due to occasional reasons, the common forwarding, the probability of the user having a correlation is less. In order to exclude the accidental factors, a weight threshold value is set>
Figure SMS_26
Reject the borderline weight +.>
Figure SMS_27
And constructing a collaborative relationship network of the events to be studied. The collaborative relationship network can be abstracted into a graph consisting of nodes and edges between nodes>
Figure SMS_28
As shown in FIG. 2, wherein->
Figure SMS_29
Representing user nodes, connecting edges between users +.>
Figure SMS_20
Behavior occurrence representing that users forward the same article source cooperatively>
Figure SMS_23
And twice. For example, for borderline weights +.>
Figure SMS_30
，/>
Figure SMS_31
And->
Figure SMS_32
For cooperating user pairs->
Figure SMS_33
Representing user +.>
Figure SMS_34
And->
Figure SMS_35
At a time interval threshold +.>
Figure SMS_21
The same article source is forwarded together within seconds, and the above-described cooperative forwarding behavior occurs 3 times.
Step two: and carrying out weighted fusion on the first-order direct adjacent similarity and the neighborhood similarity based on the global cooperative relation network so as to obtain the node comprehensive similarity.
The second step comprises the following steps:
s21: calculating first order direct proximity similarity
Constructing a synergistic relationship network
Figure SMS_36
After that, define->
Figure SMS_42
The adjacency matrix is->
Figure SMS_43
Calculation degree matrix->
Figure SMS_44
：
Figure SMS_45
Computing a synergistic relationship network->
Figure SMS_46
Laplace matrix>
Figure SMS_48
：/>
Figure SMS_37
Laplace matrix +.>
Figure SMS_38
Normalization is carried out: />
Figure SMS_39
Normalized Laplace matrix ++according to the spectral theorem>
Figure SMS_40
And (3) performing eigenvalue decomposition: />
Figure SMS_41
Wherein (1)>
Figure SMS_47
Diagonal matrix of eigenvalues, +.>
Figure SMS_49
Is a feature vector matrix.
The values of the characteristic values are ordered according to ascending order and before extraction
Figure SMS_50
Characteristic value and calculate +.>
Figure SMS_52
And feature vectors corresponding to the feature values. Will->
Figure SMS_53
The individual eigenvectors make up a matrix: />
Figure SMS_54
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_55
For the number of user nodes>
Figure SMS_56
Is the dimension of the vector. />
Figure SMS_57
Represents a user node +.>
Figure SMS_51
The dimensions represent vectors.
For the following
Figure SMS_58
Let->
Figure SMS_60
、/>
Figure SMS_61
Respectively indicate->
Figure SMS_67
Is>
Figure SMS_68
、/>
Figure SMS_69
Row vector->
Figure SMS_70
、/>
Figure SMS_59
Representing user nodes +.>
Figure SMS_62
、
Figure SMS_63
Is>
Figure SMS_64
The individual dimensions represent, user node->
Figure SMS_65
And->
Figure SMS_66
The first order direct proximity similarity calculation formula is:
Figure SMS_71
the node embedding vector similarity obtained based on the Laplace feature mapping only considers the first-order similarity of the user node pairs, and does not consider the similarity of the neighborhood structure of the user nodes.
S22: calculation improvement
Figure SMS_73
Neighborhood similarity, use ∈>
Figure SMS_78
Similarity calculation user node->
Figure SMS_80
And->
Figure SMS_83
Neighborhood similarity of (c): />
Figure SMS_84
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_85
、/>
Figure SMS_86
Representing user nodes +.>
Figure SMS_72
、/>
Figure SMS_74
Is the number of neighbor nodes; />
Figure SMS_75
Representing user node +.>
Figure SMS_76
And user node->
Figure SMS_77
Is determined by the number of common neighbors of the network. User node
Figure SMS_79
And user node->
Figure SMS_81
Having more common neighbors, +.>
Figure SMS_82
The larger the value.
Figure SMS_88
Similarity measures the similarity of two nodes based on the number of common neighbors, but the closeness of the connection between the common neighbors cannot be assessed. Suppose there are two networks +.>
Figure SMS_95
And->
Figure SMS_96
Node->
Figure SMS_97
And node->
Figure SMS_98
Having the same neighbor node in both networks, but in the network +.>
Figure SMS_99
In, node->
Figure SMS_105
And node->
Figure SMS_106
The owned common neighbors are connected more closely internally, i.e., the common neighbors have a greater network density. In this case, the number of the cells to be processed is,use->
Figure SMS_107
The similarity formula calculates the node similarity, and then +.>
Figure SMS_108
I.e. consider node +>
Figure SMS_109
And node->
Figure SMS_110
Is at the discretion of the network>
Figure SMS_111
And->
Figure SMS_112
Is equal, but a more rational estimation method should be such that nodes +.>
Figure SMS_113
And node->
Figure SMS_87
In the network->
Figure SMS_89
Is greater than in the network +.>
Figure SMS_90
Similarity in (a) and (b). The present invention therefore proposes an improvement +.>
Figure SMS_91
Similarity: />
Figure SMS_92
Wherein, the->
Figure SMS_93
Is indicated at node->
Figure SMS_94
Node->
Figure SMS_100
And the actual number of connected edges in the sub-network formed by the common neighbors; />
Figure SMS_101
，/>
Figure SMS_102
In the sub-network described above it is shown, total number of network nodes>
Figure SMS_103
Representing the maximum number of theoretically formed edges in the sub-network; />
Figure SMS_104
And the obtained network density value in the sub-network is shown.
S23: computing comprehensive similarity of nodes
Respectively to
Figure SMS_115
、/>
Figure SMS_116
Carrying out maximum value standardization, carrying out weighted fusion on the standardized numerical values to obtain the comprehensive similarity of the nodes, and ensuring the full mining and utilization of network structure information, wherein the comprehensive similarity calculation formula is as follows:
Figure SMS_117
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_118
The weight value is represented, and the value range is 0-1; />
Figure SMS_119
Is->
Figure SMS_120
Is the maximum value of (2); />
Figure SMS_121
Is->
Figure SMS_114
Is a maximum value of (a).
Step three: based on the comprehensive similarity of the nodes, the collaborative relationship network is subjected to group division by using a hierarchical division method, and each water army group participating in the social network event is obtained.
The third step comprises the following steps:
s31: constructing a group similarity formula
For any two populations
Figure SMS_122
、/>
Figure SMS_123
The similarity of the two groups is measured based on the maximum value of the comprehensive similarity of any node pair in the two groups, and the group similarity formula is as follows: />
Figure SMS_124
Wherein (1)>
Figure SMS_125
Is of the group->
Figure SMS_126
Any node in>
Figure SMS_127
Is of the group->
Figure SMS_128
Any node in the hierarchy.
S32: hierarchical method-based group partitioning
Group division of networks using hierarchical division methods, assuming there are
Figure SMS_129
The individual nodes are grouped, initially each node being considered a group. Then combining the two most similar populations into one population using a population similarity formula, co-generating +.>
Figure SMS_130
A population. The merging of the two most similar populations then continues until all nodes are merged into one population.
The whole dividing flow can be expressed as a hierarchical tree diagram, each layer represents a group dividing result of the relational network, module values corresponding to each group dividing result are traversed, and module values are selected
Figure SMS_134
The largest group division result is used as the optimal division result of the network. Modularity->
Figure SMS_136
The calculation formula of (2) is as follows: />
Figure SMS_138
；
Figure SMS_140
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_142
Representing the sum of all cooperative forwarding weights in the network; />
Figure SMS_145
Representing node->
Figure SMS_146
And node->
Figure SMS_131
Is a cooperative forwarding weight of (1); />
Figure SMS_133
Representation->
Figure SMS_135
Degree of (2), i.e. all AND nodes +.>
Figure SMS_137
The sum of the cooperative forwarding weights that occur; />
Figure SMS_139
Representing nodes
Figure SMS_141
A population to which the method belongs; if the user is->
Figure SMS_143
、/>
Figure SMS_144
Belongs to the same group, is->
Figure SMS_132
The value is 1, otherwise, the value is 0; the greater the modularity, the more closely the inside association of the naval group of division, the more sparse the association between the naval group, the naval group structure is more reasonable, divides the effect better.
After the naval group division result is obtained, the naval group leader is identified by using a majority voting method. For each group, counting the frequency of forwarding source users of users in the group, arranging the frequency in a descending order, and selecting
Figure SMS_147
The user of the frequency forwarding source is used as the group leader, i.e. the most forwarded +.>
Figure SMS_148
And (5) a user. A water army group identification algorithm description diagram based on social network cooperative forwarding behavior is shown in fig. 3.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages. Based on the same inventive concept, the embodiment of the application also provides a water army group identification device based on the social network cooperative forwarding behavior, wherein each module is used for executing each step in the embodiment corresponding to the water army group identification method.
Referring to fig. 4, the water army group identification device includes:
a cooperative relationship network module: and analyzing whether the two pairs of users forward the same article source within a very short time interval based on the social network event, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the pairs of users, extracting the cooperative forwarding relationship of all the pairs of users in the social network event, and constructing a global cooperative relationship network.
And the comprehensive similarity calculation module is used for: the method is configured to perform weighted fusion on the first-order direct adjacent similarity and the neighborhood similarity based on the global cooperative relation network so as to obtain the node comprehensive similarity.
The group dividing module: the method comprises the steps of obtaining a water army group participating in a social network event by using a hierarchical division method to divide the group of the collaborative relationship network based on the node comprehensive similarity.
The cooperative relational network module comprises:
the collaborative forwarding relation module is configured to set a keyword group and a time range of a social network event to be researched, and based on the keyword group and the time range, perform data retrieval and matching by using an API and a crawler tool to obtain social network event related data, wherein the acquired fields comprise
Figure SMS_157
(user name),>
Figure SMS_160
(user's hair->
Figure SMS_162
）、/>
Figure SMS_163
(forwarding source user name),)>
Figure SMS_164
(forwarding source article->
Figure SMS_165
）、/>
Figure SMS_166
(time of text). After the data is cleaned and preprocessed, the same article source is forwarded->
Figure SMS_149
Put the data of the same group, i.e. the transfer source article of each group of data +.>
Figure SMS_151
The values are equal. For each set of data, according to +.>
Figure SMS_153
Ascending order is performed if the user +>
Figure SMS_155
And->
Figure SMS_156
Is>
Figure SMS_158
Second, indicate that the user is about to forward the same article source in a very short time interval, at user +.>
Figure SMS_159
And->
Figure SMS_161
And a continuous edge is constructed between the two. When the user is->
Figure SMS_150
And->
Figure SMS_152
When the condition of multiple cooperative forwarding occurs, the cooperative times are accumulated, and the accumulated cooperative forwarding times are the link weight of the network +.>
Figure SMS_154
。
And the eliminating module is configured to eliminate the continuous edges with the continuous edge weight smaller than the set threshold value and construct a global cooperative relationship network. If the user
Figure SMS_170
And->
Figure SMS_172
The border weight between->
Figure SMS_174
Less, possibly due to occasional reasons, the common forwarding, the probability of the user having a correlation is less. In order to exclude the accidental factors, a weight threshold value is set>
Figure SMS_176
Removing the edge weight
Figure SMS_178
And constructing a collaborative relationship network of the events to be studied. The collaborative relationship network can be abstracted into a graph consisting of nodes and edges between nodes>
Figure SMS_180
As shown in FIG. 2, wherein +.>
Figure SMS_181
Representing user nodes, connecting edges between users +.>
Figure SMS_167
Behavior occurrence representing that users forward the same article source cooperatively>
Figure SMS_169
And twice. For example, for edge weights
Figure SMS_171
，/>
Figure SMS_173
And->
Figure SMS_175
For cooperating user pairs->
Figure SMS_177
Representing user +.>
Figure SMS_179
And->
Figure SMS_182
At a time interval threshold +.>
Figure SMS_168
The same article source is forwarded together within seconds, and the above-described cooperative forwarding behavior occurs 3 times.
The comprehensive similarity calculation module comprises:
the proximity similarity calculation module is configured to,
constructing a synergistic relationship network
Figure SMS_183
After that, define->
Figure SMS_184
The adjacency matrix is->
Figure SMS_185
Calculation degree matrix->
Figure SMS_186
。
Figure SMS_193
The method comprises the steps of carrying out a first treatment on the surface of the Computing a synergistic relationship network->
Figure SMS_195
Laplace matrix>
Figure SMS_197
：/>
Figure SMS_200
The method comprises the steps of carrying out a first treatment on the surface of the Laplace matrix +.>
Figure SMS_201
Normalization is carried out: />
Figure SMS_203
The method comprises the steps of carrying out a first treatment on the surface of the Normalized Laplace matrix according to the spectral theorem>
Figure SMS_204
And (3) performing eigenvalue decomposition: />
Figure SMS_188
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_190
Diagonal matrix of eigenvalues, +.>
Figure SMS_192
Is a feature vector matrix. Sorting the eigenvalue values in ascending order, extracting the anterior +.>
Figure SMS_194
Characteristic value and calculate +.>
Figure SMS_196
And feature vectors corresponding to the feature values. Will->
Figure SMS_198
The individual eigenvectors make up a matrix: />
Figure SMS_199
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_202
For the number of user nodes>
Figure SMS_187
Is the dimension of the vector. />
Figure SMS_189
Represents a user node +.>
Figure SMS_191
The dimensions represent vectors.
For the following
Figure SMS_205
Let->
Figure SMS_208
、/>
Figure SMS_209
Respectively indicate->
Figure SMS_211
Is>
Figure SMS_213
、/>
Figure SMS_215
Row vector->
Figure SMS_217
、/>
Figure SMS_206
Representing user nodes +.>
Figure SMS_207
、
Figure SMS_210
Is>
Figure SMS_212
Dimension representation, user node->
Figure SMS_214
And->
Figure SMS_216
The first order direct proximity similarity calculation formula is:
Figure SMS_218
a neighborhood similarity calculation module configured to,
using improvements
Figure SMS_222
Similarity is calculated: />
Figure SMS_223
The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_225
、/>
Figure SMS_227
user node +.>
Figure SMS_229
、/>
Figure SMS_231
Is the number of neighbor nodes; />
Figure SMS_232
Representing user node +.>
Figure SMS_220
And user node
Figure SMS_221
Is a common neighbor number of (a); />
Figure SMS_224
Is indicated at node->
Figure SMS_226
Node->
Figure SMS_228
And the actual number of connected edges in the sub-network formed by the common neighbors;
Figure SMS_230
，/>
Figure SMS_233
in the sub-network described above it is shown, total number of network nodes>
Figure SMS_234
Representing the maximum number of theoretically formed edges in the sub-network; />
Figure SMS_219
And the obtained network density value in the sub-network is shown.
A comprehensive similarity calculation module configured to respectively for
Figure SMS_236
、/>
Figure SMS_237
Carrying out maximum value standardization, carrying out weighted fusion on the standardized numerical values to obtain the comprehensive similarity of the nodes, and ensuring the full mining and utilization of network structure information, wherein the comprehensive similarity calculation formula is as follows: />
Figure SMS_238
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_239
The weight value is represented, and the value range is 0-1; />
Figure SMS_240
Is->
Figure SMS_241
Is the maximum value of (2);
Figure SMS_242
is->
Figure SMS_235
Is a maximum value of (a).
The group division module comprises:
a group similarity calculation module configured to,
for any two populations
Figure SMS_243
、/>
Figure SMS_244
The similarity of the two groups is measured based on the maximum value of the comprehensive similarity of any node pair in the two groups, and the group similarity formula is as follows: />
Figure SMS_245
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_246
Is of the group->
Figure SMS_247
Any node in>
Figure SMS_248
Is of the group->
Figure SMS_249
Any node in the hierarchy.
A hierarchy-based partitioning module configured to,
group division of networks using hierarchical division methods, assuming there are
Figure SMS_250
The individual nodes are grouped, initially each node being considered a group. Then combining the two most similar populations into one population using a population similarity formula, co-generating +.>
Figure SMS_251
A population. The merging of the two most similar populations then continues until all nodes are merged into one population.
The whole partitioning flow can be expressed as a hierarchical tree diagram, each layer represents a group partitioning result of the relational network, and each group partitioning result is traversedSelecting the corresponding module degree value and selecting the module degree
Figure SMS_253
The largest group division result is used as the optimal division result of the network. Modularity->
Figure SMS_255
The calculation formula of (2) is as follows: />
Figure SMS_256
；
Figure SMS_259
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_261
Representing the sum of all cooperative forwarding weights in the network; />
Figure SMS_264
Representing node->
Figure SMS_265
And node->
Figure SMS_254
Is a cooperative forwarding weight of (1); />
Figure SMS_257
Representation->
Figure SMS_258
Degree of (2), i.e. all AND nodes +.>
Figure SMS_260
The sum of the cooperative forwarding weights that occur; />
Figure SMS_262
Representing nodes
Figure SMS_263
A population to which the method belongs; if the user is->
Figure SMS_266
、/>
Figure SMS_267
Belongs to the same group, is->
Figure SMS_252
The value is 1, otherwise, the value is 0.
The greater the modularity, the more closely the inside association of the naval group of division, the more sparse the association between the naval group, the naval group structure is more reasonable, divides the effect better.
After the naval group division result is obtained, the naval group leader is identified by using a majority voting method. For each group, counting the frequency of forwarding source users of users in the group, arranging the frequency in a descending order, and selecting
Figure SMS_268
The user of the frequency forwarding source is used as the group leader, i.e. the most forwarded +.>
Figure SMS_269
And (5) a user.
It should be understood that, in the structural block diagram of the water army group identification device shown in fig. 4, each module is configured to execute each step in the embodiment corresponding to fig. 1, and each step in the embodiment corresponding to fig. 1 has been explained in detail in the foregoing embodiment, and specific reference is made to fig. 1 and related descriptions in the embodiment corresponding to fig. 1, which are not repeated herein.
Based on the same inventive concept, the embodiment of the application also provides computer equipment for realizing the water army group identification method based on the social network cooperative forwarding behavior. The implementation scheme of the solution to the problem provided by the computer device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiments of the computer device provided below may refer to the limitation of the water army group identification method based on the social network cooperative forwarding behavior hereinabove, and will not be repeated herein.
In one embodiment, a computer device, which may be a terminal, is provided that includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a water army group identification method based on social network cooperative forwarding behavior. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
In one embodiment, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the method for identifying a water army population based on social network cooperative forwarding behavior as described in the above embodiments when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements a water army population identification method based on social network cooperative forwarding behavior as described in the above embodiments.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements a water army population identification method based on social network cooperative forwarding behavior as described in the above embodiments.
The water army group identification method, the device, the computer equipment, the computer readable storage medium and the computer program product based on the social network cooperative forwarding behavior have the following beneficial effects:
1. the method adopts the social behavior mode of cooperative forwarding to identify the water army, automatically judges abnormal behaviors, and is more accurate than a method for manually constructing the characteristic to identify the water army. The implementation process of the invention is fully automatic, the forwarding data of the network event is input, the large-scale cooperative forwarding behavior in the network event can be automatically identified, the water army group is excavated, the water army leader is identified, the speed is high, the accuracy is high, and the practical application value is high in the implementation process;
2. the invention uses the novel angle of the water army group excavation to identify the water army, and the method is more accurate than the method for identifying the single water army, and the result has more explanation significance. The water army group is excavated, and meanwhile, the water army organization characteristics and the behavior patterns can be found;
3. the invention provides a novel node comprehensive similarity method, which carries out weighted fusion on first-order direct adjacent similarity and neighborhood similarity of node pairs, and fully mines and utilizes network structure information. The global cooperative relationship network is subjected to group division based on the node comprehensive similarity, so that all water army groups participating in the same network event can be accurately mined.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims (8)

1. The water army group identification method based on the social network cooperative forwarding behavior is characterized by comprising the following steps of:
step one: based on a social network event, analyzing whether every two pairs of users forward the same article source within a very short time interval, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the pairs of users, extracting the cooperative forwarding relationship of all the pairs of users in the social network event, and constructing a global cooperative relationship network;
step two: based on a global cooperative relation network, carrying out weighted fusion on the first-order direct adjacent similarity and the neighborhood similarity to obtain node comprehensive similarity;
step three: based on the comprehensive similarity of the nodes, carrying out group division on the cooperative relationship network by using a hierarchical division method to obtain all water army groups participating in social network events;
the second step comprises the following steps:
s21: constructing a synergistic relationship network
Figure QLYQS_1
After that, define->
Figure QLYQS_2
The adjacency matrix is->
Figure QLYQS_3
Calculation degree matrix->
Figure QLYQS_4
：
Figure QLYQS_5
；
Computing a synergistic relationship network
Figure QLYQS_6
Laplace matrix>
Figure QLYQS_7
：
Figure QLYQS_8
；
Will Laplace matrix
Figure QLYQS_9
Normalization is carried out:
Figure QLYQS_10
；
normalized Laplace matrix according to the spectral theorem
Figure QLYQS_11
And (3) performing eigenvalue decomposition:
Figure QLYQS_12
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_13
diagonal matrix of eigenvalues, +.>
Figure QLYQS_14
Is a feature vector matrix;
the values of the characteristic values are ordered according to ascending order and before extraction
Figure QLYQS_15
Characteristic value and calculate +.>
Figure QLYQS_16
Feature vectors corresponding to the feature values are to be +.>
Figure QLYQS_17
The individual eigenvectors make up a matrix:
Figure QLYQS_18
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_19
for the number of user nodes>
Figure QLYQS_20
Is the dimension of the vector; />
Figure QLYQS_21
Represents a user node +.>
Figure QLYQS_22
A dimension represents a vector;
for the following
Figure QLYQS_24
Let->
Figure QLYQS_26
、/>
Figure QLYQS_28
Respectively indicate->
Figure QLYQS_30
Is>
Figure QLYQS_32
、/>
Figure QLYQS_34
Row vector->
Figure QLYQS_35
、/>
Figure QLYQS_23
Representing user nodes +.>
Figure QLYQS_25
、/>
Figure QLYQS_27
Is the first of (2)
Figure QLYQS_29
The individual dimensions represent, user node->
Figure QLYQS_31
And->
Figure QLYQS_33
The first order direct proximity similarity calculation formula is:
Figure QLYQS_36
；
s22: with improvements
Figure QLYQS_37
A neighborhood similarity calculation formula: />
Figure QLYQS_38
；
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_39
、/>
Figure QLYQS_41
representing user nodes +.>
Figure QLYQS_44
、/>
Figure QLYQS_46
Is the number of neighbor nodes; />
Figure QLYQS_48
Representing user nodes
Figure QLYQS_51
And user node->
Figure QLYQS_52
Is a common neighbor number of (a); />
Figure QLYQS_40
Is indicated at node->
Figure QLYQS_42
Node->
Figure QLYQS_43
And the actual number of connected edges in the sub-network formed by the common neighbors; />
Figure QLYQS_45
，/>
Figure QLYQS_47
In the sub-network described above it is shown, total number of network nodes>
Figure QLYQS_49
Representing the maximum number of theoretically formed edges in the sub-network; />
Figure QLYQS_50
Representing the obtained network density value in the sub-network;
s23: respectively to
Figure QLYQS_53
、/>
Figure QLYQS_54
Carrying out maximum value standardization, and carrying out weighted fusion on the standardized numerical values to obtain the comprehensive similarity of the nodes, wherein the comprehensive similarity calculation formula is as follows:
Figure QLYQS_55
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_56
the weight value is represented, and the value range is 0-1; />
Figure QLYQS_57
Is->
Figure QLYQS_58
Is the most significant of (3)A large value; />
Figure QLYQS_59
Is->
Figure QLYQS_60
Is a maximum value of (a).
2. The water army group identification method based on social network cooperative forwarding behavior according to claim 1, wherein the step one includes the steps of:
s11: setting a keyword group and a time range of a social network event for the social network event to be researched, and carrying out data retrieval and matching by using an API and a crawler tool based on the keyword group and the time range to obtain related data of the social network event; after the data is cleaned and preprocessed, the source article is forwarded
Figure QLYQS_62
The data with equal values are put into the same group, and for each group of data, the data are arranged in ascending order according to the text time; if the user is->
Figure QLYQS_64
And->
Figure QLYQS_65
Is>
Figure QLYQS_66
Second, indicate that the user is about to forward the same article source in a very short time interval, at user +.>
Figure QLYQS_67
And->
Figure QLYQS_68
A connecting edge is constructed between the two; when the user is->
Figure QLYQS_69
And->
Figure QLYQS_61
When the condition of multiple cooperative forwarding occurs, the cooperative times are accumulated, and the accumulated cooperative forwarding times are the link weight of the network +.>
Figure QLYQS_63
；
S21: setting a weight threshold
Figure QLYQS_70
Reject the borderline weight +.>
Figure QLYQS_71
Constructing a collaborative relationship network of events to be studied>
Figure QLYQS_72
Collaborative relationship network->
Figure QLYQS_73
An abstraction is a graph consisting of nodes and edges between nodes.
3. The water army group identification method based on social network cooperative forwarding behavior according to claim 1, wherein the third step comprises the following steps:
s31: for any two populations
Figure QLYQS_74
、/>
Figure QLYQS_75
The similarity of the two groups is measured based on the maximum value of the comprehensive similarity of any node pair in the two groups, and the group similarity formula is as follows:
Figure QLYQS_76
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_77
is of the group->
Figure QLYQS_78
Any node in>
Figure QLYQS_79
Is of the group->
Figure QLYQS_80
Any node in (a);
s32: group division of networks using hierarchical division methods, assuming there are
Figure QLYQS_81
The individual nodes divide groups, and each node is initially regarded as a group; then combining the two most similar populations into one population using a population similarity formula, co-generating +.>
Figure QLYQS_82
A population of individuals; then continuing to merge the two most similar groups until all nodes are merged into one group;
the whole dividing flow is expressed as a hierarchical structure tree diagram, each layer represents a group dividing result of the relational network, module value corresponding to each group dividing result is traversed, and module is selected
Figure QLYQS_83
Maximum group division result is used as the optimal division result of the network, modularity is->
Figure QLYQS_84
The calculation formula of (2) is as follows: />
Figure QLYQS_85
；/>
Figure QLYQS_86
；
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_88
representing the sum of all cooperative forwarding weights in the network; />
Figure QLYQS_89
Representing node->
Figure QLYQS_92
And node->
Figure QLYQS_94
Is a cooperative forwarding weight of (1);
Figure QLYQS_96
representation->
Figure QLYQS_97
Degree of (2), i.e. all AND nodes +.>
Figure QLYQS_98
The sum of the cooperative forwarding weights that occur; />
Figure QLYQS_87
Representing node->
Figure QLYQS_90
A population to which the method belongs; if the user is->
Figure QLYQS_91
、/>
Figure QLYQS_93
Belongs to the same group, is->
Figure QLYQS_95
The value is 1, otherwise, the value is 0;
after the naval group division result is obtained, the naval group leader is identified by using a majority voting method.
4. The water army group identification device based on the social network cooperative forwarding behavior is characterized by comprising the following modules:
a cooperative relationship network module: the method comprises the steps that based on a social network event, whether every two pairs of users forward the same article source in a very short time interval is analyzed, if the occurrence times of the behaviors reach a preset threshold, a cooperative forwarding relationship exists among the pairs of users, the cooperative forwarding relationship of all the pairs of users in the social network event is extracted, and a global cooperative relationship network is constructed;
and the comprehensive similarity calculation module is used for: the method comprises the steps of carrying out weighted fusion on first-order direct adjacent similarity and neighborhood similarity based on a global cooperative relation network to obtain node comprehensive similarity;
the group dividing module: the method comprises the steps of performing group division on a collaborative relationship network by using a hierarchical division method based on node comprehensive similarity to obtain all water army groups participating in social network events;
the comprehensive similarity calculation module comprises:
the proximity similarity calculation module is configured to,
constructing a synergistic relationship network
Figure QLYQS_99
After that, define->
Figure QLYQS_100
The adjacency matrix is->
Figure QLYQS_101
Calculation degree matrix->
Figure QLYQS_102
：
Figure QLYQS_103
；
Computing a synergistic relationship network
Figure QLYQS_104
Laplace matrix>
Figure QLYQS_105
：
Figure QLYQS_106
；
Will Laplace matrix
Figure QLYQS_107
Normalization is carried out:
Figure QLYQS_108
；
normalized Laplace matrix according to the spectral theorem
Figure QLYQS_109
And (3) performing eigenvalue decomposition:
Figure QLYQS_110
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_111
diagonal matrix of eigenvalues, +.>
Figure QLYQS_112
Is a feature vector matrix;
the values of the characteristic values are ordered according to ascending order and before extraction
Figure QLYQS_113
Characteristic value and calculate +.>
Figure QLYQS_114
Feature vectors corresponding to the feature values are to be +.>
Figure QLYQS_115
The individual eigenvectors make up a matrix:
Figure QLYQS_116
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_117
for the number of user nodes>
Figure QLYQS_118
Is the dimension of the vector; />
Figure QLYQS_119
Represents a user node +.>
Figure QLYQS_120
A dimension represents a vector;
for the following
Figure QLYQS_122
Let->
Figure QLYQS_123
、/>
Figure QLYQS_125
Respectively indicate->
Figure QLYQS_127
Is>
Figure QLYQS_129
、/>
Figure QLYQS_131
Row vector->
Figure QLYQS_133
、/>
Figure QLYQS_121
Representing user nodes +.>
Figure QLYQS_124
、/>
Figure QLYQS_126
Is the first of (2)
Figure QLYQS_128
The individual dimensions represent, user node->
Figure QLYQS_130
And->
Figure QLYQS_132
The first order direct proximity similarity calculation formula is:
Figure QLYQS_134
；
a neighborhood similarity calculation module configured to,
with improvements
Figure QLYQS_135
A neighborhood similarity calculation formula:
Figure QLYQS_136
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_138
、/>
Figure QLYQS_140
representing user nodes +.>
Figure QLYQS_142
、/>
Figure QLYQS_144
Is the number of neighbor nodes; />
Figure QLYQS_146
Representing user nodes
Figure QLYQS_147
And user node->
Figure QLYQS_149
Is a common neighbor number of (a); />
Figure QLYQS_137
Is indicated at node->
Figure QLYQS_139
Node->
Figure QLYQS_141
And the actual number of connected edges in the sub-network formed by the common neighbors; />
Figure QLYQS_143
，/>
Figure QLYQS_145
In the sub-network described above it is shown, total number of network nodes>
Figure QLYQS_148
Representing the maximum number of theoretically formed edges in the sub-network; />
Figure QLYQS_150
Representing the obtained network density value in the sub-network;
the integrated similarity calculation module is configured to,
respectively to
Figure QLYQS_151
、/>
Figure QLYQS_152
Carrying out maximum value standardization, and carrying out weighted fusion on the standardized numerical values to obtain the comprehensive similarity of the nodes, wherein the comprehensive similarity calculation formula is as follows:
Figure QLYQS_153
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_154
the weight value is represented, and the value range is 0-1; />
Figure QLYQS_155
Is->
Figure QLYQS_156
Is the maximum value of (2); />
Figure QLYQS_157
Is->
Figure QLYQS_158
Is a maximum value of (a).
5. The water army group identification device based on social network cooperative forwarding behavior of claim 4, wherein the cooperative relationship network module comprises:
the collaborative forwarding relation module is configured to set a keyword group and a time range of a social network event for the social network event to be researched, and based on the keyword group and the time range, the API and the crawler tool are utilized for data retrieval and matching to obtain social network event related data; after the data is cleaned and preprocessed, the source article is forwarded
Figure QLYQS_160
The data with equal values are put into the same group, and for each group of data, the data are arranged in ascending order according to the text time; if the user is->
Figure QLYQS_162
And->
Figure QLYQS_163
Is>
Figure QLYQS_164
Second, indicate that the user is about to forward the same article source in a very short time interval, at user +.>
Figure QLYQS_165
And->
Figure QLYQS_166
A connecting edge is constructed between the two; when the user is->
Figure QLYQS_167
And->
Figure QLYQS_159
When the condition of multiple cooperative forwarding occurs, the cooperative times are accumulated, and the accumulated cooperative forwarding times are the link weight of the network +.>
Figure QLYQS_161
；
A rejection module configured to set a weight threshold
Figure QLYQS_168
Reject the borderline weight +.>
Figure QLYQS_169
Constructing a collaborative relationship network of events to be studied>
Figure QLYQS_170
Collaborative relationship network->
Figure QLYQS_171
An abstraction is a graph consisting of nodes and edges between nodes.
6. The water army group identification device based on social network cooperative forwarding behavior of claim 4, wherein the group partitioning module comprises:
a group similarity calculation module configured to,
for any two populations
Figure QLYQS_172
、/>
Figure QLYQS_173
The similarity of the two groups is measured based on the maximum value of the comprehensive similarity of any node pair in the two groups, and the group similarity formula is as follows:
Figure QLYQS_174
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_175
is of the group->
Figure QLYQS_176
Any node in>
Figure QLYQS_177
Is of the group->
Figure QLYQS_178
Any node in (a);
a hierarchy-based partitioning module configured to,
group division of networks using hierarchical division methods, assuming there are
Figure QLYQS_179
The individual nodes divide groups, and each node is initially regarded as a group; then combining the two most similar populations into one population using a population similarity formula, co-generating +.>
Figure QLYQS_180
A population of individuals; then continuing to merge the two most similar groups until all nodes are merged into one group;
the whole dividing flow is expressed as a hierarchical structure tree diagram, each layer represents a group dividing result of the relational network, module value corresponding to each group dividing result is traversed, and module is selected
Figure QLYQS_181
Maximum group division result is used as the optimal division result of the network, modularity is->
Figure QLYQS_182
The calculation formula of (2) is as follows:
Figure QLYQS_183
；/>
Figure QLYQS_184
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_186
representing the sum of all cooperative forwarding weights in the network; />
Figure QLYQS_187
Representing node->
Figure QLYQS_190
And node->
Figure QLYQS_191
Is a cooperative forwarding weight of (1);
Figure QLYQS_193
representation->
Figure QLYQS_195
Degree of (2), i.e. all AND nodes +.>
Figure QLYQS_196
The sum of the cooperative forwarding weights that occur; />
Figure QLYQS_185
Representing node->
Figure QLYQS_188
A population to which the method belongs; if the user is->
Figure QLYQS_189
、/>
Figure QLYQS_192
Belongs to the same group, is->
Figure QLYQS_194
The value is 1, otherwise, the value is 0;
after the naval group division result is obtained, the naval group leader is identified by using a majority voting method.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 3 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 3.
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