Network node importance evaluation method and device based on local and global information

Abstract

The embodiment of the invention discloses a network node importance evaluation method and device based on local and global information. The method comprises the following steps: acquiring the clustering possibility of any node according to the link condition of each node in a complex network; clustering the complex network according to the clustering possibility of each node to obtain at least one cluster; gathering all clusters to obtain a global critical node set, and obtaining the overall influence of the nodes according to the K shell value of each node in the complex network and the global critical node set; and evaluating the importance of each node in the complex network according to the overall influence. Through the mode, the importance of each node in the complex network in the network can be comprehensively known, and the application range is wide.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of network analysis, and in particular to a method and device for evaluating the importance of network nodes based on local and global information. Background Art

[0002] The ranking of network node importance is a key research topic in the field of network science. Its purpose is to discover key nodes that can affect the network structure and function to a greater extent. Designing algorithms that can quickly and accurately identify key network nodes is of great significance in both theoretical research and practical life. For example, for virus transmission networks, selectively controlling some important nodes in the network or changing their structural properties, such as vaccination, reconnecting broken edges, or repairing vulnerabilities, can effectively reduce the speed of virus transmission and reduce the scope of spread; in military supply chain networks, finding key nodes and focusing on protecting them can improve the reliability and efficiency of material support and effectively complete logistics support tasks; in social networks, influential users are selected through certain strategies to promote and market new products, so that product information can be widely disseminated in the network to increase revenue benefits.

[0003] How to use quantitative analysis methods to identify which nodes in a complex network are the most important, or to evaluate the importance of a node relative to one or more other nodes, is a hot topic in complex network research. It can be said that there is no universal algorithm for the existing node importance ranking methods, which is determined by the diversity and dynamics of complex networks. Among the existing algorithms, from the perspective of research direction, some focus on analyzing the neighborhood information and location information of a single node, some focus on analyzing the mutual influence of node quality, and some focus on changes in network structure caused by node changes; from a research perspective, although some algorithms are relatively simple to calculate, they are limited to the local information of the node, and the result errors are large. Although some algorithms comprehensively examine the global information of the node and continuously update it in iterations, they are limited to small-scale networks due to their high computational complexity. Summary of the invention

[0004] In view of the above problems, the embodiments of the present invention provide a method and device for evaluating the importance of network nodes based on local and global information, which overcomes the problem that the result error is large and can only be limited to small-scale networks.

[0005] According to one aspect of an embodiment of the present invention, a method for evaluating the importance of network nodes based on local and global information is provided, the method comprising: obtaining the clustering possibility of any node according to the link status of each node in a complex network; clustering the complex network according to the clustering possibility of each node to obtain at least one cluster; combining all clusters to obtain a global critical node set, and obtaining the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set; and evaluating the importance of each node in the complex network according to the overall influence.

[0006] Optionally, obtaining the clustering possibility of any node according to the link status of each node in the complex network includes: determining the adjacent nodes of any node according to the link status of each node in the complex network; obtaining the clustering possibility of any node v according to the following relationship i The clustering possibility is:

[0007]

[0008] Among them, pc i For node v i The clustering possibility, k i is the weight, Represents the connection node v j With node v i and the number of links between its adjacent nodes, N(i) represents the node v i The adjacent nodes of .

[0009] Optionally, the complex network is clustered according to the clustering possibility of each node to obtain at least one cluster, including: taking the node with the largest clustering possibility in the complex network as the initial node to establish an initial cluster; adding adjacent nodes in the complex network whose clustering possibility exceeds half of the maximum clustering possibility to the cluster; repeatedly adding adjacent nodes of the cluster that meet preset requirements to the cluster according to the degree of the node to generate a tightly connected cluster; setting the clustering possibility of each node in the generated cluster to 0, and repeatedly clustering other nodes in the complex network until the total number of nodes in each generated cluster reaches a preset proportion of the total number of nodes in the complex network.

[0010] Optionally, the step of repeatedly adding adjacent nodes of the cluster that meet preset requirements to the cluster according to the node degrees to generate a closely connected cluster includes: obtaining adjacent nodes v of the cluster i The number of links to the cluster and from the neighboring node v i The number of remaining edges extending to the rest of the network; if the adjacent node v i The number of links to the cluster is greater than or equal to the neighboring node v iThe number of remaining edges extending to other parts of the network is then the adjacent node v i Join the cluster; and repeat the process of adding adjacent nodes connected to the cluster in descending order of degree for a preset number of times, and adding adjacent nodes that meet preset requirements to the cluster to generate a closely connected cluster.

[0011] Optionally, clustering the complex network according to the clustering possibility of each node to obtain at least one cluster includes: setting the node with the highest degree value in each cluster as a global key node.

[0012] Optionally, the overall influence of the node is obtained according to the K-shell value of each node in the complex network and the global critical node set, including: calculating the sum of the K-shell values ​​of all adjacent nodes of any node in the complex network to obtain the local influence of the node; obtaining the indirect influence of the node on the global critical node set according to the K-shell value of each node and the global key nodes in the global critical node set; and calculating the product of the local influence of the node and the indirect influence to obtain the overall influence of the node.

[0013] Optionally, the obtaining the indirect influence of the node on the global critical node set according to the K-shell value of each node and the global key nodes in the global critical node set includes: determining the shortest path length from the node to each global key node in the global critical node set; calculating the sum of the K-shell values ​​of all adjacent nodes of any of the global key nodes to obtain the local influence of the global key node; and calculating the indirect influence of the node on the global critical node set according to the shortest path length and the local influence of the global key node by applying the following relationship:

[0014]

[0015] Among them, node v u is the global key node in the global critical node set, C is the global critical node set, d iu For node v i With the global key node v u The shortest path length between u is the global key node v u The local influence of ks m For node v m The K-shell value of node v u The set of adjacent nodes.

[0016] Based on the same inventive concept, a network node importance evaluation device based on local and global information is provided, including: a clustering possibility evaluation unit, used to obtain the clustering possibility of any node according to the link status of each node in the complex network; a clustering unit, used to cluster the complex network according to the clustering possibility of each node to obtain at least one cluster; an influence acquisition unit, used to gather all clusters to obtain a global critical node set, and obtain the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set; an importance evaluation unit, used to evaluate the importance of each node in the complex network according to the overall influence.

[0017] Based on the same inventive concept, an embodiment of the present invention further proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned method when executing the program.

[0018] Based on the same inventive concept, an embodiment of the present invention further proposes a computer storage medium, in which at least one executable instruction is stored, and the executable instruction enables a processor to execute the aforementioned method.

[0019] The embodiment of the present invention obtains the clustering possibility of any node according to the link status of each node in the complex network; clusters the complex network according to the clustering possibility of each node to obtain at least one cluster; takes the cluster to which any node belongs as the global critical node set of the node, and obtains the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set; evaluates the importance of each node in the complex network according to the overall influence, and comprehensively understands the importance of each node in the complex network, which has a wide range of applications.

[0020] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0022] Figure 1 A schematic diagram of a flow chart of a method for evaluating the importance of a network node based on local and global information provided by an embodiment of the present invention is shown;

[0023] Figure 2 A schematic diagram of a method for evaluating the importance of network nodes based on local and global information according to an embodiment of the present invention is shown;

[0024] Figure 3 A schematic diagram showing the structure of a network node importance evaluation device based on local and global information provided by an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0027] Figure 1 FIG. 1 is a flow chart showing a method for evaluating the importance of network nodes based on local and global information provided by an embodiment of the present invention. Figure 1 As shown, the network node importance evaluation based on local and global information is applied to the server, and the network node importance evaluation method based on local and global information includes:

[0028] Step S11: Obtain the clustering possibility of any node according to the link status of each node in the complex network.

[0029] In social networks, cluster structures can usually be observed, that is, nodes within a cluster show strong connections, while connections between different clusters are relatively sparse. Relatively speaking, nodes that show strong connections are more likely to form clusters. The embodiment of the present invention obtains the clustering possibility of any node based on the link conditions of each node in a complex network. The higher the clustering possibility value of a node, the higher the degree of the node and the tighter the links between its adjacent nodes, indicating that such nodes are more likely to form a clustering structure in the network.

[0030] Step S12: clustering the complex network according to the clustering possibility of each node to obtain at least one cluster.

[0031] Social consistency theory states that individuals tend to align their attitudes, beliefs, and behaviors with those of the group. Social influence plays an important role in decision-making, leading to the well-documented phenomenon known as the herd effect. This means that individuals tend to conform to the choices and behaviors of the majority, thereby promoting the effective dissemination of information within the group. Therefore, it is reasonable to assume that if a node has the ability to effectively transmit information to various groups in the network, then the influence of the node in spreading information is also huge. The main method to achieve the maximum influence dissemination in the target market is to determine the nodes that have direct connections with the nodes at the core of the network (local aspects) and the nodes that have the shortest paths to important nodes in the cluster (global aspects). To implement this strategy, the nodes in the network are clustered and at least one cluster is identified. The embodiment of the present invention clusters the complex network according to the clustering possibility of each node obtained in step S11 to obtain at least one cluster.

[0032] Step S13: Gather all clusters to obtain a global critical node set, and obtain the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set.

[0033] The larger the K-shell value of a node, the more and more important its neighboring nodes are, and the more important they are to the target node. The information initiated from the node is less likely to be confined to the surrounding area, and it is possible to achieve widespread dissemination of information through the cluster to which it belongs. In this embodiment of the present invention, the cluster to which any node belongs is used as the global critical node set of the node, and the overall influence of the node is obtained according to the K-shell value of each node and the global critical node set.

[0034] Step S14: Evaluate the importance of each node in the complex network according to the overall influence.

[0035] The greater the overall influence of a node, the higher the importance of the node. According to the order of the overall influence of the nodes, the importance sequence of each node in the complex network is obtained. In this way, if information dissemination is required, it can be transmitted to the nodes with greater overall influence according to the importance sequence to achieve widespread dissemination of information. If it is a military supply chain network, the nodes with greater overall influence can be protected to improve the stability of the network, the reliability and efficiency of material support, and effectively complete the logistics support tasks. Similarly, other network applications are applied according to the importance sequence of the nodes.

[0036] The embodiment of the present invention clusters the complex network according to the clustering possibility of the nodes, classifies the complex network into multiple clusters, and then obtains the overall influence of each node according to the K-shell value of each node and the global critical node set, and then determines the importance of each node in the complex network. In the importance evaluation process, the local information related to the number of links between each node and the adjacent nodes and the global information of the cluster to which the node belongs are fully considered, which can fully understand the importance of each node in the complex network and has a wide range of applications.

[0037] The bandwagon effect refers to the tendency of an individual's attitudes, beliefs, and behaviors to be consistent with those of the group. Using social conformity theory to study the problem of maximizing influence is helpful to understand the phenomenon of influence propagation. In an embodiment of the present invention, a group of nodes whose connections are denser than those with other nodes in the network is first detected. To this end, nodes with many neighbors are first selected in the network for potential clustering. In step S11, optionally, the adjacent nodes of any node are determined according to the link status of each node in the complex network; the adjacent nodes of any node v are obtained according to the following relationship i The clustering possibility is:

[0038]

[0039] Among them, pc i For node v i The clustering possibility, k i is the weight, Represents the connection node v j With node v i and the number of links between its adjacent nodes, N(i) represents the node v i The adjacent nodes of .

[0040] In the embodiment of the present invention, in order to determine the nodes that have direct connections with nodes located in the core of the network (local aspect) and the nodes that have the shortest paths to important nodes in the cluster (global aspect), it is first intended to identify the clusters in the network and find influential nodes in each cluster and designate them as global key nodes of the entire network.

[0041] In step S12, optionally, the node with the largest clustering possibility in the complex network is first used as the initial node to establish an initial cluster. max ), select a node with the highest degree as the initial node, establish an initial cluster, represented by C cluster. Then add the adjacent nodes whose clustering probability in the complex network exceeds half of the maximum clustering probability to the cluster. max Half of the adjacent nodes join cluster C, PC max Indicates the maximum PC value of the nodes in the network.

[0042] Based on the cluster obtained above, the adjacent nodes of the cluster that meet the preset requirements are repeatedly added to the cluster according to the node degree, so as to generate a closely connected cluster. The adjacent nodes of a cluster refer to the adjacent nodes of any node in the cluster. Optionally, the adjacent nodes v of the cluster are obtained. i The number of links to the cluster and from the neighboring node v i The number of remaining edges extending to the rest of the network; if the adjacent node v i The number of links to the cluster is greater than or equal to the neighboring node v i The number of remaining edges extending to other parts of the network is then the adjacent node v i Add the adjacent nodes connected to the cluster in descending order of degree, repeat the preset number of times, add the adjacent nodes that meet the preset requirements to the cluster, and generate a tightly connected cluster. The existing cluster is expanded by incorporating adjacent nodes on the basis of core nodes. i When , the total degree is divided into two parts: Represents the connection node v i The number of links to subgroup C, Represents the slave node v i The number of remaining edges extending to the rest of the network. If a node v i Satisfy the conditions It will be included in the subgroup. The neighboring nodes connected to the subgroup are explored in descending order of degree. This iterative process is repeated multiple times, preferably 3 times, to eventually produce a tightly connected group. This completes the clustering of a cluster.

[0043] After a cluster is generated, the clustering possibility of each node in the generated cluster is set to 0, and the clustering of other nodes in the complex network is repeated until the total number of nodes in each generated cluster reaches a preset proportion of the total number of nodes in the complex network. The clustering possibility of each node in the generated cluster is set to 0, and the node with the greatest clustering possibility among other nodes in the complex network is used as a new initial node, and a new cluster is generated using the same method as before. If the number of all nodes in all generated clusters reaches a preset proportion λ of the total number of nodes in the complex network, the clustering of the complex network ends. The preset proportion λ is a value between 0 and 1, which can be set as needed, and is not specifically limited here. Preferably, λ=0.6.

[0044] After the complex network is clustered and multiple clusters are obtained, the node with the highest degree value in each cluster is set as the global key node. After the complex network is clustered and multiple clusters are obtained, for any cluster, the node with the highest degree value in the cluster is set as the global key node. In this way, there are as many global key nodes as there are clusters. This network cluster detection algorithm is very efficient and can ensure the relative dispersion and importance of the selected global key nodes. Once the node v i Once the initiated information successfully reaches these global key nodes, it can easily trigger explosive information dissemination within the network.

[0045] In the embodiment of the present invention, the premise for measuring the local influence of a node is that when a node has more connections with neighboring nodes located in the core of the network, it will play a more powerful propagation function. The nodes are sorted according to their local influence and the influence of global key nodes, so as to fully understand the importance of the nodes in the network.

[0046] The local influence of a node mainly depends on the number and importance of its neighboring nodes. The more and more important the neighboring nodes are, the more important the target node is. The local influence of a node can be measured by neighborhood coreness centrality. The basic assumption of neighborhood coreness centrality is that if most of the neighboring nodes of a node are at the core of the network, then the information sent from this node will spread to the entire network faster. Before step S13, the network is first decomposed using the K-shell decomposition method to obtain the local influence of any node v in the network. i K shell value ks i .

[0047] In step S13, optionally, the sum of the K-shell values ​​of all adjacent nodes of any node in the complex network is first calculated to obtain the local influence of the node. The local influence of the node satisfies the following relationship:

[0048]

[0049] Among them, LI i For node v i The local influence of ks j For node v j The K-shell value of node v i The greater the local influence of a node, the more it ensures that information originating from the node is less likely to be confined to the surrounding area.

[0050] In addition, when nodes are closer to global key nodes in complex networks, information is more likely to spread to different clusters in complex networks through these nodes, thereby achieving widespread dissemination of information. Considering the influence of nodes from multiple perspectives can improve the accuracy and effectiveness of influential node identification.

[0051] The embodiment of the present invention also obtains the indirect influence of the node on the global critical node set based on the K-shell value of each node and the global critical nodes in the global critical node set. Optionally, determine the shortest path length from the node to each global critical node in the global critical node set; then calculate the sum of the K-shell values ​​of all adjacent nodes of any of the global critical nodes to obtain the local influence of the global critical node; then calculate the indirect influence of the node on the global critical node set based on the shortest path length and the local influence of the global critical node using the following relationship:

[0052]

[0053] Among them, node v u is the global key node in the global critical node set, C is the global critical node set, d iu For node v i With the global key node v u The shortest path length between u is the global key node v u The local influence of ks m For node v m The K-shell value of node v u The set of adjacent nodes.

[0054] Finally, the product of the local influence of the node and the indirect influence is calculated to obtain the overall influence of the node. The overall influence of the node is calculated according to the following relationship:

[0055]

[0056] Among them, GAC i For node v i The overall influence of C is the global critical node set, ks j For node v j The K-shell value of node v i The larger the GAC value, the greater the propagation influence of the node in the network.

[0057] The embodiment of the present invention identifies the groups in the network by determining the nodes that are directly connected to the nodes located in the core of the network (local aspect) and the nodes that have the shortest paths to the important nodes in the cluster (global aspect), and finds out the influential nodes in each group and designates them as the global key nodes of the entire network. When a node has more connections with the adjacent nodes located in the core of the network, it will play a more powerful propagation function. The nodes are sorted according to the local influence of the nodes and the influence of the global key nodes. In this way, not only the local influence of the nodes is taken into account, but also the indirect influence of the nodes on the key nodes in the network cluster is taken into account, so as to fully understand the importance of the nodes in the network.

[0058] Schematic diagram of a network node importance evaluation method based on local and global information according to an embodiment of the present invention Figure 2 For a complex network G = (V, E), V is a node in the complex network, and E is a link between nodes. The K-shell decomposition method is used to decompose the network to obtain the K-shell value ks of each node in the network. Then the neighborhood core centrality of each node in the network is calculated, that is, the local influence LI of the node. At the same time, group detection and global key node selection are performed on the complex network G. Taking λ = 1.0 as an example, all nodes in the complex network G are clustered into multiple clusters, and the nodes with the highest degree value in each cluster are set as global key nodes. Finally, the overall influence GAC of each node in the complex network is calculated based on the local influence LI of each node in the complex network and each global key node, thereby realizing the importance evaluation of each node in the complex network.

[0059] In summary, the embodiments of the present invention obtain the clustering possibility of any node according to the link status of each node in the complex network; cluster the complex network according to the clustering possibility of each node to obtain at least one cluster; combine all clusters to obtain a global critical node set, and obtain the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set; evaluate the importance of each node in the complex network according to the overall influence, so as to fully understand the importance of each node in the complex network and have a wide range of applications.

[0060] The above specific embodiments of the present invention are described. In some cases, the actions or steps recorded in the embodiments of the present invention can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] Based on the same concept, an embodiment of the present invention also provides a network node importance evaluation device based on local and global information. Applicable to a server. Figure 3 As shown, the network node importance evaluation device based on local and global information includes: a clustering possibility evaluation unit, a clustering unit, an influence acquisition unit and an importance evaluation unit.

[0062] A clustering possibility evaluation unit is used to obtain the clustering possibility of any node according to the link status of each node in the complex network;

[0063] A clustering unit, configured to cluster the complex network according to the clustering possibility of each node to obtain at least one cluster;

[0064] An influence acquisition unit, used to gather all clusters to obtain a global critical node set, and to obtain the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set;

[0065] The importance evaluation unit is used to evaluate the importance of each node in the complex network according to the overall influence.

[0066] For the convenience of description, the above device is described as various modules according to their functions. Of course, when implementing the embodiment of the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0067] The device of the above embodiment is applied to the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0068] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of the above embodiments is implemented.

[0069] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the method described in any of the above embodiments.

[0070] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 401, a memory 402, an input / output interface 403, a communication interface 404, and a bus 405. The processor 401, the memory 402, the input / output interface 403, and the communication interface 404 are connected to each other in communication within the device through the bus 405.

[0071] The processor 401 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided by the method embodiment of the present invention.

[0072] The memory 402 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 402 can store an operating system and other application programs. When the technical solution provided by the method embodiment of the present invention is implemented by software or firmware, the relevant program code is stored in the memory 402 and called and executed by the processor 401.

[0073] The input / output interface 403 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0074] The communication interface 404 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0075] The bus 405 includes a path for transmitting information between various components of the device (eg, the processor 401 , the memory 402 , the input / output interface 403 , and the communication interface 404 ).

[0076] It should be noted that, although the above device only shows the processor 401, the memory 402, the input / output interface 403, the communication interface 404 and the bus 405, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present invention, and does not necessarily include all the components shown in the figure.

[0077] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0078] This application is intended to cover all such substitutions, modifications and variations that fall within the broad scope of all embodiments. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of this disclosure.

Claims

1. A network node importance evaluation method based on local and global information, characterized in that: The method comprises: Obtain the clustering possibility of any node based on the link status of each node in the complex network; Clustering the complex network according to the clustering possibility of each node to obtain at least one cluster; Gather all clusters to obtain a global critical node set, and obtain the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set; The importance of each node in the complex network is evaluated according to the overall influence.

2. The method according to claim 1, characterized in that The step of obtaining the clustering possibility of any node according to the link status of each node in the complex network includes: Determine the adjacent nodes of any node based on the link status of each node in the complex network; Get any node v according to the following relationship i Clustering possibility: Among them, pc i For node v i The clustering possibility, k i is the weight, Represents the connection node v j With node v i and the number of links between its adjacent nodes, N(i) represents the node v i The adjacent nodes of .

3. The method according to claim 1, characterized in that The step of clustering the complex network according to the clustering possibility of each node to obtain at least one cluster includes: Taking the node with the greatest clustering possibility in the complex network as the initial node, establishing an initial cluster; Adding adjacent nodes in the complex network whose clustering likelihood exceeds half of the maximum clustering likelihood to the cluster; Repeating adding adjacent nodes of the cluster that meet preset requirements to the cluster according to the degrees of the nodes to generate a closely connected cluster; The clustering possibility of each node in the generated cluster is set to 0, and the clustering is repeated for other nodes in the complex network until the total number of nodes in each generated cluster reaches a preset proportion of the total number of nodes in the complex network.

4. The method according to claim 3, characterized in that The step of repeatedly adding adjacent nodes of the cluster that meet preset requirements to the cluster according to the degree of the nodes to generate a closely connected cluster includes: Get the neighboring nodes v of the cluster i The number of links to the cluster and from the neighboring node v i The number of remaining edges extending to the rest of the network; If the adjacent node v i The number of links to the cluster is greater than or equal to the neighboring node v i The number of remaining edges extending to other parts of the network is then the adjacent node v i joining the cluster; Adjacent nodes connected to the cluster are arranged in descending order of degree for a preset number of times, and adjacent nodes that meet preset requirements are added to the cluster to generate a closely connected cluster.

5. The method according to claim 1, characterized in that After clustering the complex network according to the clustering possibility of each node to obtain at least one cluster, the method includes: The node with the highest degree value in each cluster is set as the global key node.

6. The method according to claim 5, characterized in that The obtaining the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set includes: Calculate the sum of the K-shell values ​​of all adjacent nodes of any node in the complex network to obtain the local influence of the node; Obtaining the indirect influence of the node on the global critical node set according to the K-shell value of each node and the global key nodes in the global critical node set; The product of the local influence of the node and the indirect influence is calculated to obtain the overall influence of the node.

7. The method according to claim 6, characterized in that The obtaining the indirect influence of the node on the global critical node set according to the K-shell value of each node and the global key node in the global critical node set includes: Determine the shortest path length from the node to each global critical node in the global critical node set; Calculate the sum of the K-shell values ​​of all adjacent nodes of any of the global key nodes to obtain the local influence of the global key node; The indirect influence of the node on the global critical node set is calculated according to the shortest path length and the local influence of the global key node using the following relationship: Among them, node v u is the global key node in the global critical node set, C is the global critical node set, d iu For node v i With the global key node v u The shortest path length between u is the global key node v u The local influence of ks m For node v m The K-shell value of node v u The set of adjacent nodes.

8. A network node importance evaluation device based on local and global information, characterized in that: The device comprises: A clustering possibility evaluation unit is used to obtain the clustering possibility of any node according to the link status of each node in the complex network; A clustering unit, configured to cluster the complex network according to the clustering possibility of each node to obtain at least one cluster; An influence acquisition unit, used to gather all clusters to obtain a global critical node set, and to obtain the overall influence of the node according to the K-shell value of each node in the complex network and the global critical node set; The importance evaluation unit is used to evaluate the importance of each node in the complex network according to the overall influence.

9. 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, the method according to any one of claims 1 to 7 is implemented.

10. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute the method according to any one of claims 1 to 7.

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