A structural entropy-based complex network node influence ranking method and system

Abstract

The present invention relates to a method and system for ranking the influence of nodes in a complex network based on structural entropy, and belongs to the technical field of social network analysis. The present invention includes: constructing a social network graph based on a data set, using two-dimensional structural entropy to discover communities, and obtaining stable overlapping communities; calculating the influence of nodes within the community and the influence of nodes across communities, and linearly combining the influence of nodes within the community and the influence of nodes across communities to obtain a comprehensive influence value for each node; sorting all nodes according to the comprehensive influence value, and identifying key nodes with the greatest influence in the social network. The present invention measures the changes in the two-dimensional structural entropy of nodes within and between communities, evaluates their importance in the social network, and thereby identifies and ranks key nodes, providing a method for effectively identifying and ranking key nodes for obtaining important nodes in a social network.

Description

TECHNICAL FIELD

[0001] The application relates to a complex network node influence ranking method and system based on structural entropy, and belongs to the technical field of social network analysis. BACKGROUND

[0002] With the development of social network field, information transmission and interaction among people become more convenient and efficient. In the social network, the ranking of node influence not only plays an important role in information transmission and public opinion guidance, but also has wide application in viral marketing, public security and social management. How to accurately evaluate and rank the influence of nodes is crucial to optimize the function and utility of social networks.

[0003] At present, there are mainly two methods for identifying key nodes in the network. One is a method based on local attributes, and common local methods include degree centrality, local structural entropy and the like. The degree centrality is the most direct measurement index for describing the centrality of a node, and the size of the degree centrality is used to represent the influence of the node. The local structural entropy considers the information of first-order and second-order adjacent nodes, and utilizes the local structural attributes of the entire network node instead of the properties of the node itself. The other is a method based on global attributes, and common methods include betweenness centrality, PageRank algorithm and the like. The betweenness centrality describes the frequency of information passing through the node when the information is transmitted in the social network, and it can better reflect the topological characteristics of the node. The PageRank algorithm uses neighbor nodes to describe the influence of the node. However, the nodes selected by the method based on the local only consider local information, and the influence of the node is only reflected in the internal community of the aggregation, which is easy to cause the omission of the overall information. However, the methods based on the global have problems such as high computational complexity and neglecting node attributes when dealing with large-scale networks. SUMMARY

[0004] The application provides a complex network node influence ranking method and system based on structural entropy, which utilizes two-dimensional structural entropy to measure the influence change of nodes in different community structures, comprehensively considers the local authority of nodes in the belonging community and the influence breadth across the community, and provides a method for effectively identifying and ranking key nodes.

[0005] The technical scheme of the application is as follows: a complex network node influence ranking method based on structural entropy, the method comprising:

[0006] Step 1, constructing a social network graph according to a data set, performing community discovery by using two-dimensional structural entropy, obtaining stable overlapping communities, so that each node and community is in a stable state, and ensuring that each network node belongs to one or more communities;

[0007] Step2, the computing node influence in the community, node across the community influence, and linear combination of node influence in the community and node influence across the community, get each node comprehensive influence value; The influence of the node is composed of two parts, one part is the influence of the node in the community, the other part is the influence of the node across the community. The influence in the community is the change of the structure entropy of the community where the node leaves, and the influence across the community is the sum of the structure entropy of all target communities after the node joins the target community;

[0008] Step3, according to the comprehensive influence value, sort all nodes, and identify the key nodes with the maximum influence in the social network.

[0009] Further, the Step1 includes:

[0010] Step1.1, select a data set containing social network data, which includes nodes and relationships (edges) between nodes, use social network data to construct a graph structure, use NetworkX to create a graph, add nodes and edges to the graph;

[0011] Step1.2, use a two-dimensional structure entropy-based community discovery algorithm to obtain multiple overlapping communities, when the two-dimensional structure entropy of each community remains in a stable state, maintain the existing structure of the community, calculate the two-dimensional structure entropy of each community and the two-dimensional structure entropy of the whole network, obtain the adjacent community matrix of the target node, and obtain the stable overlapping community.

[0012] Further, the Step2 includes:

[0013] Step2.1, calculate the two-dimensional structure entropy of each community, obtain the adjacent community matrix of the target node, when the node moves from the existing community to the target community, the volume and boundary edge number of the original community when the node is in the original community are g in And v in Then, after the node leaves the original community, the community volume and community boundary edge number after the node leaves the community are g in And v in The influence of the node in the community after leaving the community is ΔSE in ;

[0014] g in ' = g in +d-2n in (1)

[0015] v in ' = v in +d (2)

[0016]

[0017] where d is the number of edges connected to the node; n in represents the number of neighbors of the node in the current or target community, that is, the number of connections of the node with other nodes in the community; vol is the volume of the entire graph, that is, the sum of the degrees of all nodes in the graph;

[0018] Step 2.2, according to the adjacent community matrix of the node, the target community matrix of the node is traversed in turn, for the target community, the volume and the number of boundary edges of the community before the node joins are g cross and v cross , respectively, and the volume and the number of boundary edges of the community after the node joins the target community are g cross ' and v cross ', respectively, after updating the volume and the number of boundary edges of the community, the node moves from the current community to the target community or becomes a community alone, for each node after moving, the influence of the node across the community is calculated, that is, ΔSE cross ;

[0019] g cross ' = g cross -d+2n in (4)

[0020] v cross ' = v cross -d (5)

[0021]

[0022] Step 2.3, the influence of the node in the community and the influence of the node across the community are weighted and summed to obtain the comprehensive influence of the node i;

[0023] The influence of the node is composed of the influence of the node in the community and the influence of the node across the community, but the proportion of the two is not necessarily equal, a network influence factor a is selected to balance the weight of the two, and the comprehensive influence of the node i is:

[0024]

[0025] ΔSE in represents the influence of the node in the community, that is, the authority of the node in the community; ΔSE cross represents the influence of the node across the community, the node not only has influence on the community, but also has certain influence on other communities; adj_div represents the set of all other target communities;

[0026] Step2.4, according to the IC propagation model, set alpha in [0, 1], step length is 0.01, in turn, the network influence factor alpha is traversed, the final node number after the IC propagation model is propagated, the network influence factor alpha is determined on the basis of the most propagation node number on multiple data sets, the influence of the influence in the community and the influence across the community on the node influence is determined, then step2.3 is executed.

[0027] Further, the Step3 comprises:

[0028] Step3.1, after obtaining the comprehensive influence of each node in the community according to the network influence factor, the nodes in the social network are sorted according to the comprehensive influence of the nodes, the greater the node influence, the more important the node in the social network, when the influence maximization propagation is performed, the node with higher ranking has stronger propagation ability, and finally the comprehensive influence sorting result of each node is obtained; the node list after sorting is:

[0029] V sorted =SORT desc (V,key=INF(i)) (8)

[0030] Wherein, V sorted is the node list after sorting, the nodes in the list are arranged in descending order of comprehensive influence; SORT desc is a sorting function, indicating descending order sorting of nodes; V is a set or list containing all nodes, also indicating all nodes, key sorting function parameter, indicating sorting nodes according to INF value. The node list is a set of all nodes in the social network. Through the sorting operation, the nodes in the node list are arranged in descending order of their comprehensive influence, forming a sorted list, that is, V sorted .

[0031] The application uses two-dimensional structure entropy for community discovery and calculates the influence of nodes in the existing community and the target community on the basis of the community discovery result, including the two-dimensional structure entropy of the current community and the two-dimensional structure entropy of the entire network; after the node leaves the original community and joins the target community, the entropy changes of the node original community, the target community and the entire network are recalculated; the entropy change values of each community are linearly combined to obtain the influence of each node, and the nodes are sorted; the application measures the two-dimensional structure entropy changes of the nodes in the community and between the communities, evaluates the importance of the nodes in the social network, and thus identifies and sorts the key nodes, thereby providing an effective method for obtaining important nodes in the social network.

[0032] The application further provides a system for ranking influence of nodes in a complex network based on structural entropy, comprising a module for executing the method for ranking influence of nodes in a complex network based on structural entropy.

[0033] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for ranking influence of nodes in a complex network based on structural entropy when executing the program.

[0034] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for ranking influence of nodes in a complex network based on structural entropy.

[0035] The application further provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method for ranking influence of nodes in a complex network based on structural entropy.

[0036] The application has the following beneficial effects:

[0037] 1. The application solves the problem of missing overall information due to the limitations of previous methods in identifying key nodes in a network, as local attribute methods such as degree centrality and local structural entropy only consider local information of nodes, and global attribute methods have high computational complexity, are not suitable for large-scale networks, and ignore node attributes.

[0038] 2. The application accurately evaluates the comprehensive influence of each node by calculating the entropy change when the node moves from the original community to the target community, and combining the entropy change of the entire network. This method can more accurately identify key nodes in social networks, providing a comprehensive analysis perspective and helping to understand network structure and node roles.

[0039] 3. The application provides a flexible network analysis tool by linear combination and adjustment of weight parameters, which adapts to different network structures and research needs. While improving the accuracy of community discovery, it optimizes network structure, enhances network stability, and supports precise intervention strategies, such as applications in social media, public health, and network security.

[0040] 4. Compared with traditional methods, the application not only considers local information of nodes, but also integrates global information of social networks, effectively supplementing the problem of missing overall information caused by relying only on local information. In addition, the method significantly improves the speed of processing nodes in large-scale networks and reduces computational complexity.

[0041] 5、By comparing the number of common nodes of different methods, the consistency of the nodes selected by the method of sorting nodes according to influence based on two-dimensional structure entropy and the nodes selected by other methods is verified; according to the number of final activated nodes of the nodes obtained by different methods through the IC propagation model, the effectiveness of the influence nodes selected by the method of sorting nodes according to influence based on two-dimensional structure entropy in the IC propagation model is proved;

[0042] 6、The application proposes an influence sorting method considering the influence of local and global attributes, which defines the node influence through the influence of nodes in the community and the influence across the community and sorts the nodes according to importance, effectively overcoming the limitations of previous methods;

[0043] 7、The method not only has innovation in theory, but also shows extremely high practical value in practical application, especially has wide application prospect in the fields of network structure analysis, information diffusion prediction and network security. BRIEF DESCRIPTION OF DRAWINGS

[0044] Fig. 1 is a specific structure schematic diagram of the recognition model in the application;

[0045] Fig. 2 is the consistency comparison verification result of the top 10 nodes of the facebook social network and the CA-GrQc social network obtained by the method of the application and the nodes obtained by other methods. DETAILED DESCRIPTION

[0046] Embodiment 1: as shown in the figure, a complex network node influence sorting method based on structure entropy, the method comprises: Figs. 1-2

[0047] Step1、According to the data set, a social network graph is constructed, and two-dimensional structure entropy is used for community discovery to obtain stable overlapping communities, so that each node and community is in a stable state, and it is ensured that each network node belongs to one or more communities;

[0048] Further, the Step1 comprises:

[0049] Step1.1、Select a data set containing social network data, which includes nodes and relationships (edges) between nodes, use social network data to construct a graph structure, use NetworkX network analysis library to create a graph, add nodes and edges to the graph;

[0050] ​Step1.2, using a community discovery algorithm based on two-dimensional structure entropy to obtain a plurality of overlapping communities, when the two-dimensional structure entropy of each community remains in a stable state, maintaining the existing structure of the community, calculating the two-dimensional structure entropy of each community and the two-dimensional structure entropy of the entire network, obtaining the adjacent community matrix of the target node, and obtaining stable overlapping communities.

[0051] Step2, calculating the influence of the node in the community, the influence of the node across the community, and linearly combining the influence of the node in the community and the influence of the node across the community to obtain a comprehensive influence value of each node; the influence of the node is composed of two parts, one part is the influence of the node in the community, and the other part is the influence of the node across the community. The influence in the community is the change amount of the structure entropy of the community where the node is located after the node leaves the community, and the influence across the community is the sum of the change amounts of the structure entropy of all target communities after the node joins the target community;

[0052] Further, the Step2 includes:

[0053] Step2.1, calculating the two-dimensional structure entropy of each community to obtain the adjacent community matrix of the target node, when the node moves from the existing community to the target community, the volume and the number of boundary edges of the original community when the node is in the original community are g in and v in , respectively, then after the node leaves the original community, the volume and the number of boundary edges of the community after the node leaves are g in and v in , respectively, and the influence of the node in the community after leaving the community is ΔSE in ;

[0054] g in ' = g in +d-2n in (1)

[0055] v in ' = v in +d (2)

[0056]

[0057] Wherein, d is the number of edges connected to the node; n in represents the number of neighbors of the node in the current or target community, that is, the number of connections of the node with other nodes in the community; vol is the volume of the entire graph, that is, the sum of the degrees of all nodes in the graph;

[0058] Step2.2, according to the adjacent community matrix of the node, traversing the target community matrix of the node in sequence, for the target community, the volume and the number of boundary edges of the community before the node joins are g cross and vcross , the community volume and the number of community boundary edges after the node joins the target community are g cross ' and v cross ' respectively, after updating the volume and the number of boundary edges of the community, the node moves from the current community to the target community or becomes a community alone, for each node after the movement, the influence of the node across the community is calculated, that is, ΔSE cross ;

[0059] g cross ' = g cross -d+2n in (4)

[0060] v cross ' = v cross -d (5)

[0061]

[0062] Step2.3, the influence of the node in the community and the influence of the node across the community are weighted and summed to obtain the comprehensive influence of the node i;

[0063] The influence of the node is composed of the influence of the node in the community and the influence across the community, but the proportion of the two is not necessarily equal. A network influence factor a is selected to balance the weight of the two, and the comprehensive influence of the node i is obtained as follows:

[0064]

[0065] ΔSE in represents the influence of the node in the community, that is, the authority of the node in the community; ΔSE cross represents the influence of the node across the community. The node not only has influence on the community, but also has certain influence on other communities; adj_div represents the set of all other target communities;

[0066] Step2.4, according to the IC propagation model, set a ∈ [0, 1], the step is 0.01, and the network influence factor a is traversed in turn to obtain the final node number after the IC propagation model propagation, determine the network influence factor a with the most propagated node number on multiple data sets, thereby determine the influence proportion of the influence in the community and the influence across the community on the influence of the node, and then execute Step2.3.

[0067] Step3, according to the comprehensive influence value, sort all nodes to identify the key nodes with the maximum influence in the social network.

[0068] Further, the Step3 comprises:

[0069] Step3.1, after obtaining the comprehensive influence of each node in the community according to the network influence factor, the nodes in the social network are sorted according to the comprehensive influence of the nodes, the greater the node influence, the more important the node in the social network, when the influence maximization propagation is carried out, the node with higher ranking has stronger propagation ability, and finally the comprehensive influence ranking result of each node is obtained; the node list after sorting is:

[0070] V sorted =SORT desc (V,key=INF(i)) (8)

[0071] Wherein, V sorted is the node list after sorting, the nodes in the list are arranged in descending order of comprehensive influence; SORT desc is a sorting function, indicating descending order sorting of nodes; V is a set or list containing all nodes, also indicating all nodes, key sorting function parameter, indicating sorting nodes according to INF value. The node list is a set of all nodes in the social network. Through the sorting operation, the nodes in the node list are arranged in descending order of their comprehensive influence, forming a sorted list, that is, V sorted .

[0072] The application also provides a complex network node influence sorting system based on structural entropy, which comprises:

[0073] The overlapping community module is used for constructing a social network graph according to a data set, discovering communities by using two-dimensional structural entropy, obtaining stable overlapping communities, so that each node and community is in a stable state, and ensuring that each network node belongs to one or more communities;

[0074] The node influence module is used for calculating the influence of the node in the community and the influence of the node across the community, and linearly combining the influence of the node in the community and the influence of the node across the community to obtain the comprehensive influence value of each node. The influence of the node is composed of two parts, one part is the influence of the node in the community, and the other part is the influence of the node across the community. The influence in the community is the change amount of the structural entropy of the community where the node leaves, and the influence across the community is the sum of the change amounts of the structural entropies of all target communities where the node joins;

[0075] The sorting algorithm module is used for sorting all nodes according to the comprehensive influence value, and identifying the key nodes with the maximum influence in the social network.

[0076] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for ranking influence of nodes in a complex network based on structural entropy when executing the program.

[0077] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned method for ranking influence of nodes in a complex network based on structural entropy when executed by a processor.

[0078] The application further provides a computer program product comprising a computer program, wherein the computer program implements the above-mentioned method for ranking influence of nodes in a complex network based on structural entropy when executed by a processor.

[0079] After obtaining the ranking results of the influence of each node in the network, we select the top 10 nodes or the top 20 nodes for comparison, and also select the nodes as an initial node set, and simulate the propagation process through the IC propagation model to obtain the number of finally influenced nodes.

[0080] In order to illustrate the effect of the application, three groups of comparative experiments are set up. The first group of experiments verifies the consistency of the nodes selected by the method for ranking influence of nodes based on two-dimensional structural entropy and the nodes selected by other methods, the second group of experiments verifies the effectiveness of the influence nodes selected by the method for ranking influence of nodes based on two-dimensional structural entropy in the IC propagation model, and the last group of experiments verifies the importance of the nodes selected by the method for ranking influence of nodes based on two-dimensional structural entropy in the entire network.

[0081] (1) The consistency of the nodes selected by the method for ranking influence of nodes based on two-dimensional structural entropy and the nodes selected by other methods;

[0082] In comparison with other methods for selecting influential nodes in social networks, we select the top 10 nodes in the data set, and view the common nodes and independent nodes of the top 10 nodes selected by different methods. In order to facilitate observation, we have made a display on the Venn diagram. As shown in the following Fig. 2 We selected the top 10 nodes of the facebook social network and the CA-GrQc social network data sets.

[0083] Analysis Fig. 2 It can be seen that the top 10 nodes obtained by the method of the application are mostly the same as the nodes obtained by other methods. As shown in the following Fig. 2 In (a), in the facebook network, 7 of the top 10 nodes obtained by our method overlap with other methods, and at most 7 of the nodes obtained by other methods overlap with the method itself. In (b), in the CA-GrQc network, 6 of the top 10 nodes obtained by our method overlap with other methods, and at most 6 of the nodes obtained by other methods overlap with the method itself.Fig. 2 The first 10 nodes obtained by the method of the application according to influence in the CA-GrQc network in (b) are all recognized by other methods, 9 nodes of the LE method and the DC method overlap with the method itself, and compared with other methods, the consistency of the nodes obtained by the method of the application with the nodes obtained by other methods is higher.

[0084] (2) Effectiveness of the influence nodes selected by the method of ranking nodes according to two-dimensional structure entropy in the IC propagation model;

[0085] The second group of experiments verifies the effectiveness of the influence nodes selected by the method of ranking nodes according to two-dimensional structure entropy in the IC propagation model, that is, the number of activated nodes obtained by comparing the number of activated nodes obtained by other methods after the influence nodes obtained by the method are propagated in the IC model, and the more the final number of activated nodes, the more influential the nodes selected by the method are, and the experimental results are shown in Table 1:

[0086] Table 1 is the effectiveness verification of different methods (units)

[0087]

[0088] From Table 1, it can be seen that the number of nodes propagated by the method of the application on different data sets, wherein, on the data sets email, CA-GrQc, LastFMAsia, nethepht, and Epinions, the number of final activated nodes obtained by the method of the application after the nodes selected by the method are simulated in the IC propagation model is the most. On the Facebook data set, the number of final activated nodes obtained by the DC method after the nodes selected by the method are simulated in the IC propagation model is the most, which is 11 nodes more than the number of nodes obtained by the method of the application. However, on the other 5 data sets, the number of final activated nodes obtained by the method is more than that of other methods. Therefore, the number of final activated nodes verifies the effectiveness of the influence nodes selected by the method of the application in the IC propagation model.

[0089] (3) Importance of the nodes selected by the method of ranking nodes according to two-dimensional structure entropy in the entire network;

[0090] The third group of experiments verifies the importance of the nodes selected by the method of ranking nodes according to two-dimensional structure entropy in the entire network. In this group of experiments, the influence of each node in the social network is evaluated by the network efficiency reduction ratio, a certain proportion of high-influence nodes are selected for simulation network attack, and by removing these nodes and their related edges, the change of network connectivity is observed. By comparing the network efficiency reduction ratio before and after the attack, the importance of the attacked nodes in the network can be quantified, wherein a significant decrease in network efficiency indicates that the node has critical influence, and the experimental results are shown in Table 2:

[0091] Table 2 is the network decline ratio (%) of different methods

[0092]

[0093] From the analysis of Table 2, it can be seen that the network decline ratio of the method of the present application is higher than that of all other methods when the node removal ratio is 2%, 3%, 5%, 6%, 7%, 8%, 9% and 10%, which shows that the method of the present application calculates the influence of the node by comprehensively considering the local attribute of the node and the global attribute of the social network, and sorts the nodes according to the influence. In the experiment, the influence of each node in the social network is examined by evaluating the network efficiency decline ratio, and the results show that the selected node has a significant influence on the connectivity of the entire network. The greater the network efficiency decline ratio, the higher the importance of the selected node to the network. Therefore, it can be proved that the node influence sorting method based on two-dimensional structure entropy of the present application can effectively identify the nodes that play a key role in the network.

[0094] Through the above experimental data, the present application proves the superiority of the node influence sorting method based on two-dimensional structure entropy in selecting effective nodes. Compared with the traditional method, the present application not only considers the local information of the node, but also integrates the global information of the social network, thereby effectively supplementing the problem of missing overall information caused by relying only on local information. In addition, the present application significantly improves the speed of processing nodes in large-scale networks and reduces the computational complexity. By comparing the number of common nodes of different methods, the consistency of the nodes selected by the method of sorting the influence of the nodes based on two-dimensional structure entropy and the nodes selected by other methods is verified; according to the number of final activated nodes of the nodes obtained by different methods through the IC propagation model, the effectiveness of the influence nodes selected by the method of sorting the influence of the nodes based on two-dimensional structure entropy in the IC propagation model is proved; finally, the important nodes in the attack network are simulated on the data set, which shows the importance of the nodes selected by the method of sorting the influence of the nodes based on two-dimensional structure entropy in the entire network. The present application proposes an influence sorting method that comprehensively considers local and global attributes, which defines the influence of the node by the influence of the node within the community and the influence across the community, and sorts the nodes according to the importance, effectively overcoming the limitations of previous methods. Therefore, the method not only has innovation in theory, but also shows high practical value in practical application, and has a wide application prospect in the fields of network structure analysis, information diffusion prediction and network security, etc.

[0095] The specific embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the present application.

Claims

1. A method for ranking the influence of nodes in complex networks based on structural entropy, characterized by: The method comprises: Step 1: Construct a social network graph based on the data set and use two-dimensional structural entropy to discover communities and obtain stable overlapping communities. Step 2: Calculate the node's influence within the community and its cross-community influence, and perform a linear combination of the node's influence within the community and the node's cross-community influence to obtain the comprehensive influence value of each node. Step 3: Sort all nodes according to their comprehensive influence values ​​and identify the key nodes with the greatest influence in the social network. Step 2 includes: Step 2.

1. Calculate the two-dimensional structural entropy of each community and obtain the adjacent community matrix of the target node. When the node moves from the existing community to the target community, the volume and number of boundary edges of the original community are respectively and , then after the node leaves the original community, the community volume and the number of community boundary edges after the node leaves the community are and , the influence of the node in the community after leaving the community is ; (1); (2); (3); in, is the number of edges the node is connected to, represents the number of neighbors of the node in the current or target community, is the volume of the entire graph; Step 2.2: According to the node's adjacent community matrix, the node target community matrix is ​​traversed in sequence. For the target community, the volume and number of boundary edges of the community before the node joins are and , the community volume and the number of community boundary edges after the node joins the target community are and , the node moves from the current community to the target community or becomes a separate community. For each node after the move, the influence of the node across communities is calculated, which is ; (4); (5); (6); Step 2.3: Take the weighted sum of the node’s influence within the community and the node’s influence across communities to obtain the comprehensive influence of node i; Select a network influence factor α to balance the weights of the two, and the comprehensive influence of node i is obtained as: (7); Represents the influence of the node in the community, Represents the influence of nodes across communities, represents the set of all other target communities; Step 2.4: According to the IC propagation model, the network influence factor α is traversed in sequence to obtain the final number of nodes after propagation through the IC propagation model. The network influence factor α with the largest number of propagated nodes on multiple data sets is determined. From this, the proportion of influence within the community and cross-community influence on the node influence is determined, and then Step 2.3 is executed.

2. The method for ranking the influence of complex network nodes based on structural entropy according to claim 1 is characterized by: Step 1 includes: Step 1.

1. Select a dataset containing social network data, build a graph structure using the social network data, use the network analysis library NetworkX to create a graph, and add nodes and edges to the graph; Step 1.2: Use a community discovery algorithm based on two-dimensional structural entropy to obtain multiple overlapping communities. When the two-dimensional structural entropy of each community remains stable, the existing structure of the community is maintained to obtain stable overlapping communities.

3. The method for ranking the influence of complex network nodes based on structural entropy according to claim 1 is characterized by: Step 3 includes: After obtaining the comprehensive influence of each node in the community based on the network influence factor, the nodes in the social network are sorted according to the comprehensive influence of the nodes to obtain the comprehensive influence ranking results of each node; the sorted node list is: (8); in, is the sorted list of nodes, is a sorting function, V is a set or list containing all nodes, It is a parameter in the sorting function, indicating that the nodes are sorted according to the INF value.

4. A system for ranking the influence of complex network nodes based on structural entropy, characterized by: The system comprises: a module for executing the method according to any one of claims 1-3.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements a complex network node influence ranking method based on structural entropy as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements a complex network node influence ranking method based on structural entropy as described in any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it implements a complex network node influence ranking method based on structural entropy as described in any one of claims 1 to 3.

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