A key node identification method and device, electronic equipment and storage medium

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for identifying critical nodes. The method includes: determining multiple indicator values ​​for any node based on multiple indicators, whereby the multiple indicators are used to evaluate the importance of the node; determining the comprehensive weight corresponding to any indicator using the analytic hierarchy process (AHP) and entropy weight method; determining the node's importance ranking value based on the multiple indicator values ​​and the comprehensive weights corresponding to the multiple indicators for any node; and identifying critical nodes based on the node importance ranking values ​​of all nodes. This invention solves the problem of the one-sidedness of existing critical node identification methods in related technologies.

Description

Technical Field

[0001] This invention relates to the field of key node identification in topology graphs, and more particularly to a key node identification method, apparatus, electronic device, and storage medium. Background Technology

[0002] Network survivability typically refers to the impact of node or edge failures on the overall network connectivity. Due to human and natural factors, nodes or links in communication networks may fail, reducing network survivability. Therefore, assessing node importance in topological networks has high practical value. Currently, social sciences assess node importance based on the idea that "importance is equivalent to salience." Social analysis considers a node's importance to be the degree of salience it possesses due to its connections with other nodes; centrality is one of the most important and widely used methods for analyzing social networks. Systems science, based on the idea that "destructiveness is equivalent to importance," considers the degree of disruption to network connectivity caused by node deletion to be equivalent to the node's importance. It determines the importance of a node by observing changes in network connectivity and other indicators after removing a set of nodes.

[0003] In real-world networks, the attribute characteristics of nodes, the topological features of the network, and the local and global position of a node are all crucial for identifying key nodes. Using a single indicator for calculations across different network topologies is highly limiting. Therefore, existing key node identification methods suffer from limitations. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for identifying key nodes, in order to at least solve the problem that key node identification methods in related technologies are one-sided.

[0005] According to a first aspect of the present invention, a method for identifying key nodes is provided. The method includes: determining multiple indicator values ​​for any node based on multiple indicators, wherein the multiple indicators are used to evaluate the importance of the node; determining the comprehensive weight corresponding to any indicator using the analytic hierarchy process (AHP) and the entropy weight method; determining a node importance ranking value based on the multiple indicator values ​​of the any node and the comprehensive weight corresponding to the multiple indicators; and identifying key nodes based on the node importance ranking values ​​of all nodes.

[0006] Optionally, determining the comprehensive weight corresponding to any indicator using the analytic hierarchy process (AHP) and the entropy weight method includes: determining the first weight corresponding to any indicator using the AHP; determining the second weight corresponding to any indicator using the entropy weight method; and determining the comprehensive weight corresponding to any indicator based on the arithmetic square root of the product of the first weight and the second weight corresponding to any indicator.

[0007] Optionally, determining the first weight corresponding to any indicator using the analytic hierarchy process (AHP) includes: constructing a hierarchical structure, wherein the target layer of the hierarchical structure is key node identification, the criterion layer of the hierarchical structure includes centrality indicators and importance indicators, the indicator layer of the hierarchical structure includes node connectivity, node betweenness, and proximity centrality, the node connectivity and node betweenness are the factor layers of the centrality indicators, and the proximity centrality is the factor layer of the importance indicators; establishing a judgment matrix corresponding to the hierarchical structure; and determining the first weight corresponding to any indicator based on the hierarchical single ranking and the hierarchical overall ranking.

[0008] Optionally, determining the second weight corresponding to any indicator using the entropy weight method includes: normalizing the indicator values ​​of all nodes corresponding to any indicator; determining the information value corresponding to any indicator based on the normalized indicator values ​​of all nodes corresponding to any indicator; and determining the second weight of any indicator based on the information values ​​of all indicators.

[0009] Optionally, determining the node importance ranking value based on multiple indicator values ​​of any node and the comprehensive weights corresponding to the multiple indicators includes: obtaining the node betweenness numbers of all nodes and the inter-node dependency between any two nodes, wherein the node betweenness number is the proportion of the number of shortest paths passing through the target node to the total number of shortest paths, and the inter-node dependency represents the degree of dependence of the target node on any non-target node; calculating the node importance ranking value at multiple time points using an iterative algorithm based on multiple indicator values ​​of any node, multiple comprehensive weights corresponding to the multiple indicator values, a state transition matrix, a node importance ranking matrix, and a time factor, wherein the state transition matrix and the node importance ranking matrix are initialized based on the inter-node dependency between any two nodes and the node betweenness numbers of all nodes; when the difference between the node importance ranking value at the previous time point and the next time point is less than a preset standard value, the node importance ranking value at the next time point is used as the node importance ranking value of any node.

[0010] Optionally, the time factor is determined based on the ratio of the first product to the second product, wherein the first product is the product of the joining time of any node and the damping factor, and the second product is the product of the sum of the joining times of all nodes and the number of connections of any node, wherein the joining time of a node is the difference between the current time and the joining time of the node.

[0011] Optionally, identifying key nodes based on the node importance ranking values ​​of all nodes includes: sorting the node importance ranking values ​​of all nodes from largest to smallest to obtain a sorting result; and selecting the node corresponding to the node importance ranking value in the first preset position of the sorting result as the key node.

[0012] According to a second aspect of the present invention, a key node identification device is also provided, characterized in that the device comprises: a first determining module, configured to determine multiple indicator values ​​of any node based on multiple indicators, the multiple indicators being used to evaluate the importance of the node; a second determining module, configured to determine the comprehensive weight corresponding to any indicator using the analytic hierarchy process and the entropy weight method; a third determining module, configured to determine the node importance ranking value based on the multiple indicator values ​​of the any node and the comprehensive weight corresponding to the multiple indicators; and an identification module, configured to identify key nodes based on the node importance ranking values ​​of all nodes.

[0013] Optionally, the second determining module includes: a first determining unit, used to determine the first weight corresponding to any indicator using the analytic hierarchy process; a second determining unit, used to determine the second weight corresponding to any indicator using the entropy weight method; and a third determining unit, used to determine the comprehensive weight corresponding to any indicator based on the arithmetic square root of the product of the first weight and the second weight corresponding to any indicator.

[0014] Optionally, the first determining unit includes: a first construction submodule, used to construct a hierarchical structure, wherein the target layer of the hierarchical structure is key node identification, the criterion layer of the hierarchical structure includes centrality indicators and importance indicators, the indicator layer of the hierarchical structure includes node connection number, node betweenness number, and proximity centrality, wherein the node connection number and node betweenness number are factor layers of the centrality indicators, and the proximity centrality is a factor layer of the importance indicators; a second construction submodule, used to establish a judgment matrix corresponding to the hierarchical structure; and a first determining submodule, used to determine the first weight corresponding to any indicator based on the hierarchical single sort and the hierarchical overall sort.

[0015] Optionally, the second determining unit includes: a normalization submodule, used to normalize the index values ​​of all nodes corresponding to any index; a second determining submodule, used to determine the information value corresponding to any index based on the normalized index values ​​of all nodes corresponding to any index; and a third determining submodule, used to determine the second weight of any index based on the information values ​​of all indicators.

[0016] Optionally, the third determining module includes: an acquisition unit, configured to acquire the node betweenness numbers corresponding to all nodes and the inter-node dependency between any two nodes, wherein the node betweenness number is the proportion of the number of shortest paths passing through the target node to the total number of shortest paths, and the inter-node dependency represents the degree of dependence of the target node on any non-target node; a calculation unit, configured to calculate the node importance ranking value at multiple time points using an iterative algorithm based on multiple indicator values ​​of any node, multiple comprehensive weights corresponding to the multiple indicator values, a state transition matrix, a node importance ranking matrix, and a time factor, wherein the state transition matrix and the node importance ranking matrix are initialized based on the inter-node dependency between any two nodes and the node betweenness numbers corresponding to all nodes; and a comparison unit, configured to use the node importance ranking value at the next time point as the node importance ranking value of any node when the difference between the node importance ranking value at the previous time point and the next time point is less than a preset standard value.

[0017] Optionally, the time factor is determined based on the ratio of the first product to the second product, wherein the first product is the product of the joining time of any node and the damping factor, and the second product is the product of the sum of the joining times of all nodes and the number of connections of any node, wherein the joining time of a node is the difference between the current time and the joining time of the node.

[0018] Optionally, the identification module includes: a sorting unit, used to sort the node importance ranking values ​​of all nodes from largest to smallest to obtain a sorting result; and a selection unit, used to select the nodes corresponding to the node importance ranking values ​​of the first preset positions in the sorting result as key nodes.

[0019] According to a third aspect of the present invention, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used to store a computer program; and the processor is used to execute the key node identification method of any of the above embodiments by running the computer program stored in the memory.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to execute the key node identification method of any of the above embodiments at runtime.

[0021] In this embodiment of the invention, multiple indicator values ​​are determined for any node based on multiple indicators, which are used to evaluate the importance of the node. The comprehensive weight corresponding to any indicator is determined using the Analytic Hierarchy Process (AHP) and the entropy weight method. The node importance ranking value is determined based on the multiple indicator values ​​of any node and the comprehensive weight corresponding to the multiple indicators. Key nodes are identified based on the node importance ranking values ​​of all nodes. By using multiple indicators to calculate the node importance ranking value and then using the node importance ranking value for key node identification, the problem of one-sided key node identification caused by using a single indicator to evaluate node importance is avoided. The weights of multiple indicators are jointly determined by the AHP and the entropy weight method, combining subjective and objective approaches to make the weight values ​​of each indicator more reasonable. This achieves the goal of comprehensively and reasonably evaluating node importance, improves the accuracy of key node identification, and solves the problem of one-sidedness in key node identification methods in related technologies.

[0022] In this embodiment of the invention, multiple indicator values, their corresponding comprehensive weights, a state transition matrix, a node importance ranking matrix, and a time factor are used to calculate the node importance ranking values ​​at multiple time points using an iterative algorithm. The value at which the iteration results converge is used as the node importance ranking value for key node identification, further improving the accuracy of key node identification. Introducing the time factor increases the probability of random walks after adding new nodes, thus accelerating the convergence speed. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the hardware environment for an optional key node identification method according to an embodiment of the present invention;

[0026] Figure 2 This is a flowchart illustrating an optional key node identification method according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of an optional hierarchical structure constructed according to the analytic hierarchy process according to an embodiment of the present invention;

[0028] Figure 4This is a schematic diagram of the overall process of an optional key node identification method according to an embodiment of the present invention;

[0029] Figure 5 This is a structural block diagram of an optional key node identification device according to an embodiment of the present invention;

[0030] Figure 6 This is a structural block diagram of an optional electronic device according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that in the description of this invention, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection through an intermediate medium; a connection within two elements; a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0033] According to one aspect of the present invention, a key node identification method is provided. Optionally, in this embodiment, the above-described key node identification method can be applied to, for example... Figure 1 In the hardware environment shown. For example... Figure 1As shown, terminal 102 may include memory 104, processor 106, and display 108 (optional component). Terminal 102 can communicate with server 112 via network 110. Server 112 can provide services (such as application services) to the terminal or clients installed on the terminal. Database 114 can be set up on or independently of server 112 to provide data storage services to server 112. In addition, server 112 may run a processing engine 116, which can be used to execute the steps performed by server 112.

[0034] Optionally, terminal 102 may be, but is not limited to, a terminal capable of computing data, such as a mobile terminal (e.g., a mobile phone, tablet computer), a laptop computer, a PC (Personal Computer), etc. The aforementioned network may include, but is not limited to, a wireless network or a wired network. The wireless network includes Bluetooth, Wi-Fi (Wireless Fidelity), and other networks that enable wireless communication. The aforementioned wired network may include, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), and a local area network (LAN). The aforementioned server 112 may include, but is not limited to, any hardware device capable of computing.

[0035] Furthermore, in this embodiment, the aforementioned key node identification method can also be applied to, but is not limited to, a powerful independent processing device without requiring data interaction. For example, the processing device can be, but is not limited to, a powerful terminal device; that is, the various operations in the aforementioned key node identification method can be integrated into a single independent processing device. The above is merely an example, and this embodiment does not impose any limitations on it.

[0036] Optionally, in this embodiment, the above-described key node identification method can be executed by server 112, by terminal 102, or jointly by server 112 and terminal 102. The key node identification method of this embodiment can also be executed by a client installed on terminal 102.

[0037] Taking the application of key node identification methods to the central processing unit as an example, Figure 2 This is a flowchart illustrating an optional key node identification method according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process of this method may include the following steps:

[0038] Step S201: Determine multiple indicator values ​​for any node based on multiple indicators. These multiple indicators are used to evaluate the importance of the node. Optionally, the key node identification method will be explained using a directed topology graph containing multiple nodes as an example. The multiple indicators can be indicators used to evaluate the importance of a node, such as the number of node connections, node betweenness, and proximity centrality. Any node has a corresponding indicator value under any indicator. For example, node a can have multiple indicator values ​​corresponding to multiple indicators such as node connection value, node betweenness value, and proximity centrality value.

[0039] Step S202: Determine the comprehensive weight corresponding to any indicator using the Analytic Hierarchy Process (AHP) and the entropy weight method. Optionally, determining the weights corresponding to multiple indicators using the AHP is subjective, while determining the weights corresponding to multiple indicators using the entropy weight method is more objective. Therefore, the subjective weights determined by the AHP and the objective weights determined by the entropy weight method are combined as the comprehensive weights of the indicators, making the calculation of indicator weights more reasonable.

[0040] Step S203: Determine the node importance ranking value based on multiple indicator values ​​of any node and the comprehensive weights corresponding to the multiple indicators. Optionally, the node importance ranking can be... NIR (Node Importance Ranking) indicates the ranking value of node importance. NIR Value, such as NIR (a) represents the node importance ranking value of node a. The node importance ranking value can be determined by the sum of the product of multiple indicator values ​​of the node and the comprehensive weights corresponding to the multiple indicators.

[0041] Step S204: Identify key nodes based on the node importance ranking values ​​of all nodes. Optionally, after determining the node importance ranking values ​​of each node, since nodes with higher node importance ranking values ​​are more important, key nodes can be identified based on the node importance ranking values ​​of all nodes.

[0042] In this embodiment of the invention, multiple indicator values ​​are determined for any node based on multiple indicators, which are used to evaluate the importance of the node. The comprehensive weight corresponding to any indicator is determined using the Analytic Hierarchy Process (AHP) and the entropy weight method. The node importance ranking value is determined based on the multiple indicator values ​​of any node and the comprehensive weight corresponding to the multiple indicators. Key nodes are identified based on the node importance ranking values ​​of all nodes. By using multiple indicators to calculate the node importance ranking value and then using the node importance ranking value for key node identification, the problem of one-sided key node identification caused by using a single indicator to evaluate node importance is avoided. The weights of multiple indicators are jointly determined by the AHP and the entropy weight method, combining subjective and objective approaches to make the weight values ​​of each indicator more reasonable. This achieves the goal of comprehensively and reasonably evaluating node importance, improves the accuracy of key node identification, and solves the problem of one-sidedness in key node identification methods in related technologies.

[0043] As an optional embodiment, determining the comprehensive weight corresponding to any indicator using the analytic hierarchy process (AHP) and the entropy weight method includes: determining the first weight corresponding to any indicator using the AHP; determining the second weight corresponding to any indicator using the entropy weight method; and determining the comprehensive weight corresponding to any indicator based on the arithmetic square root of the product of the first weight and the second weight. Optionally, the comprehensive weight corresponding to any indicator... W i The following formula can be used to calculate:

[0044]

[0045] In the formula, W i Represents the overall weight of any indicator. X i This represents the first weight of any indicator determined using the analytic hierarchy process (AHP). Y i This represents the second weight determined using the entropy weighting method for any indicator.

[0046] As an optional implementation, the first weight corresponding to any indicator is determined using the analytic hierarchy process (AHP), including: constructing a hierarchical structure, where the target layer of the hierarchical structure is key node identification, the criterion layer of the hierarchical structure includes centrality indicators and importance indicators, the indicator layer of the hierarchical structure includes node connectivity, node betweenness, and proximity centrality, node connectivity and node betweenness are the factor layers for centrality indicators, and proximity centrality is the factor layer for importance indicators; establishing a judgment matrix corresponding to the hierarchical structure; and determining the first weight corresponding to any indicator based on the hierarchical single ranking and the hierarchical overall ranking.

[0047] Optionally, Figure 3This is a schematic diagram of an optional hierarchical structure constructed according to the Analytic Hierarchy Process (AHP) according to an embodiment of the present invention, such as... Figure 3 As shown, the Analytic Hierarchy Process (AHP) generally divides the decision-making objectives, considered factors, and decision objects into three layers based on their interrelationships: the objective layer, the criterion layer, and the indicator layer. In a hierarchical structure, adjacent layers are also referred to as the objective layer (higher layer) and the factor layer (lower layer). In this embodiment, the decision-making objective of the hierarchical structure is key node identification. Key node identification considers two indicators: centrality and importance. Centrality indicators include node connectivity and betweenness, while importance indicators include proximity centrality. When determining the weights between factors at each layer, a consistent matrix method is used to compare factors pairwise, employing a relative scale to improve accuracy. Based on the importance levels and their assigned values ​​in the proportional scaling table, a judgment matrix is ​​established for each factor at each layer in the hierarchical structure according to the pairwise comparison results. In this embodiment, the importance indicator is considered more critical than the centrality indicator, thus establishing the judgment matrices for the objective layer and the criterion layer. P As shown below:

[0048]

[0049] In the formula, P 12 This indicates the comparison results between centrality and importance. P 21 This indicates the comparison results between importance and centrality. P 11 This indicates the result of a comparison between centralities. P 22 This represents the comparison result of importance versus importance. Similarly, the centrality index and its index layer's judgment matrix... Q As shown below:

[0050]

[0051] In the formula, Q 12 This represents the comparison result between the node betweenness and the number of node connections. Q 21 This represents the comparison result between the number of node connections and the node betweenness. Q 11 This indicates the result of comparing node betweenness with node betweenness. Q 22 This indicates the comparison result between the number of node connections and the number of node connections.

[0052] Hierarchical single sorting uses the method of solving the eigenvectors of the judgment matrix to obtain the priority weight of each element in each level relative to a certain element in the previous level. According to matrix theory, when the order of a matrix is ​​the same as its largest eigenvalue and all other eigenvalues ​​are zero, the matrix has perfect consistency. When the matrix does not perfectly satisfy the consistency requirement, an index is needed to measure the quality of the matrix's consistency. The calculation method is as follows:

[0053]

[0054] In the formula, CI This represents a consistency indicator. λ max It is the largest eigenvalue of the matrix. m It is the order of the matrix. CI The smaller the value, the greater the consistency. After hierarchical single ranking and consistency test, the criterion layer weights for centrality are 0.333, and for importance, 0.667. Under the centrality criterion, the weights for the number of node connections are 0.333, and for node betweenness, 0.667. Under the importance criterion, the weight for proximity centrality is 1. The overall hierarchical ranking of the analytic hierarchy process calculates the relative importance of all factors at a certain level for identifying key nodes at the target level. For example, the weight of the number of node connections for identifying key nodes (i.e., the first weight corresponding to the number of node connections) is equal to the weight of the node connection criterion layer (0.333) multiplied by the weight of the centrality criterion layer (0.333), approximately equal to 0.111. Similarly, the first weight corresponding to node betweenness is 0.222, and the first weight corresponding to proximity centrality is 0.667.

[0055] As an optional embodiment, the entropy weight method is used to determine the second weight corresponding to any indicator, including: normalizing the indicator values ​​of all nodes corresponding to any indicator; determining the information value corresponding to any indicator based on the normalized indicator values ​​of all nodes corresponding to any indicator; and determining the second weight of any indicator based on the information values ​​of all indicators. Optionally, the entropy weight method is based on information entropy and determines the objective weight, i.e., the second weight, of any indicator through the relationship between node indicator values. First, the indicator values ​​of all nodes corresponding to any indicator are normalized using the following formula:

[0056]

[0057] In the formula, Indicates the first index under any index t The result of normalizing the index values ​​of each node; The first to be normalized t The index values ​​of each node. The information entropy corresponding to any index is calculated using the following formula:

[0058]

[0059] In the formula, E i Indicates the first i The information entropy corresponding to each indicator c It is a constant. n Indicates the number of nodes. The second weight of any indicator is calculated using the following formula:

[0060]

[0061] In the formula, Y i Indicates the first i The second weight corresponding to each indicator E i Indicates the first i The information entropy corresponding to each indicator k This refers to the number of indicators. It can be understood that the objective weight of any indicator calculated using the entropy weight method, i.e., the second weight, is... Y i The subjective weight of any indicator calculated by the analytic hierarchy process, i.e., the first weight. X i Combining these methods can make the weight of any indicator more reasonable, thus making the calculation results of the node importance ranking value more convincing.

[0062] As an optional embodiment, the node importance ranking value is determined based on multiple indicator values ​​of any node and the comprehensive weights corresponding to the multiple indicators. This includes: obtaining the node betweenness numbers of all nodes and the inter-node dependency between any two nodes. The node betweenness number is the proportion of the number of shortest paths passing through the target node to the total number of shortest paths. The inter-node dependency represents the degree of dependence of the target node on any non-target node. Based on multiple indicator values ​​of any node, multiple comprehensive weights corresponding to the multiple indicator values, the state transition matrix, the node importance ranking matrix, and the time factor, an iterative algorithm is used to calculate the node importance ranking value at multiple time points. The state transition matrix and the node importance ranking matrix are initialized based on the inter-node dependency between any two nodes and the node betweenness numbers of all nodes, respectively. When the difference between the node importance ranking value at the previous time point and the next time point is less than a preset standard value, the node importance ranking value at the next time point is used as the node importance ranking value of any node.

[0063] Optionally, assume that the multiple metrics are the number of node connections, node betweenness, and proximity centrality. Wherein, node... a Number of node connections The betweenness of a node can be represented by the sum of its outgoing and incoming links. It is the proportion of all shortest paths in a graph or network that pass through the target node, reflecting the node's role and influence within the entire graph or network. a node betweenness The following formula can be used to calculate:

[0064]

[0065] In the formula, Represents a node j , k The number of shortest paths between them. Represents a node j , k The shortest path between them passes through the target node, i.e., node. a The number of.

[0066] Proximity centrality is the reciprocal of the sum of the shortest distances from a target node to all other nodes in the graph. If the shortest distances from a target node to all other nodes in the graph are small, then its proximity centrality is high, meaning it is closer to the geometric center. a proximity centrality The following formula can be used to calculate:

[0067]

[0068] In the formula, d aj The target node is the node. a The shortest distance to other nodes. Node dependency is expressed as a proportion by summing the betweenness factors of the nodes. For example, nodes... i For nodes a The inter-node dependency can be calculated using the following formula:

[0069]

[0070] In the formula, B ij (a) Representative node i and j Among all shortest paths, the nodes passed through are a The proportion of the number of paths to the total number of shortest paths. The node importance ranking value is calculated using an iterative algorithm based on the following formula:

[0071] In the formula, This represents the node importance ranking value of node a. The time factor; n The number of nodes; , , They are nodes a Proximity centrality, node betweenness, and number of node connections; These are the combined weights for proximity centrality, node betweenness, and number of node connections, respectively. Here is the state transition matrix. This is a ranking matrix based on node importance. and They are shown below:

[0072]

[0073] In the state transition matrix, Indicates from node Transfer to node The probability is initialized using the dependency between nodes, i.e. = D ij .

[0074]

[0075] In the node importance ranking matrix, Represents a node n The node importance ranking value is initialized using the node betweenness, i.e. When using an iterative algorithm to calculate the node importance ranking, because the time factor includes the value at the current moment, the ranking of each node will change after multiple iterations. NIR The value will tend to stabilize, i.e., converge. This value will be used as the standard for evaluating the importance of nodes, i.e., the value after convergence. NIR The value serves as the final ranking value for a node's importance.

[0076] As an optional embodiment, the time factor is determined based on the ratio of the first product to the second product. The first product is the product of the joining time of any node and the damping factor, and the second product is the product of the sum of the joining times of all nodes and the number of connections of any node. The node joining time is the difference between the current time and the node joining time.

[0077] Optionally, time factor The following formula can be used for calculation:

[0078]

[0079] In the formula, the time factor That is, with the probability of accessing the next node after leaving the chain, This represents the probability of a random walk. t 0 indicates the current time. ，t aRepresents a node a When adding a topological structure, i.e., a directed topological graph. That is, nodes a The time of joining That is, the sum of the times when all nodes joined; L(a) It is a node a The number of connections, d It is the damping factor; as can be seen from this formula, for nodes with shorter joining times and larger node connections, The smaller the value, the greater the probability of a random walk. In this embodiment, the introduction of a time factor increases the probability of a random walk after adding a new node, thereby accelerating the convergence speed.

[0080] As an optional embodiment, identifying key nodes based on the node importance ranking values ​​of all nodes includes: sorting all nodes from largest to smallest importance ranking values ​​to obtain a ranking result; and selecting the nodes corresponding to the top preset importance ranking values ​​in the ranking result as key nodes. Optionally, after determining the node importance ranking value of each node, all nodes are sorted from largest to smallest importance ranking values, and then the top preset values ​​in the ranking result are selected as key nodes. NIR The node corresponding to the value is used as the key node. The first preset position can be set according to the specific situation. For example, if three key nodes need to be selected, the first preset position is the first three.

[0081] As an optional embodiment, Figure 4 This is a schematic diagram of the overall process of an optional key node identification method according to an embodiment of the present invention, as shown below. Figure 4 As shown, the number of node connections, node betweenness, and proximity centrality are calculated. The inter-node dependency is calculated based on the node betweenness, and then the node importance ranking matrix and state transition matrix are initialized using the node betweenness and inter-node dependency. The weights of node connections, node betweenness, and proximity centrality—that is, the comprehensive weights corresponding to node connections, node betweenness, and proximity centrality—are determined by combining the analytic hierarchy process (AHP) and the entropy weight method. The node importance ranking value is then calculated using the formula, i.e., an iterative algorithm is used to calculate the importance ranking value of each node. NIR The value is then used to determine whether the node importance ranking values ​​of all nodes converge. If the node importance ranking values ​​of all nodes converge, then... NIR If the values ​​converge, sort the nodes and select the first preset position of the sorted result. NIR The node corresponding to the value is designated as the key node; if a node exists... NIR If the values ​​do not converge, recalculate the combined weights corresponding to the number of node connections, node betweenness, and proximity centrality, and substitute them into the formula to calculate the node importance ranking value until the node importance ranking values ​​of all nodes converge.

[0082] According to another aspect of the present invention, a key node identification device is also provided. Figure 5 This is a structural block diagram of an optional key node identification device according to an embodiment of the present invention, such as... Figure 5 As shown, the device may include: a first determining module 501, used to determine multiple indicator values ​​for any node based on multiple indicators, wherein the multiple indicators are used to evaluate the importance of the node; a second determining module 502, used to determine the comprehensive weight corresponding to any indicator using the analytic hierarchy process and the entropy weight method; a third determining module 503, used to determine the node importance ranking value based on the multiple indicator values ​​of any node and the comprehensive weight corresponding to the multiple indicators; and an identification module 504, used to identify key nodes based on the node importance ranking values ​​of all nodes.

[0083] It should be noted that the first determining module 501 in this embodiment can be used to execute the above step S201, the second determining module 502 in this embodiment can be used to execute the above step S202, the third determining module 503 in this embodiment can be used to execute the above step S203, and the identification module 504 in this embodiment can be used to execute the above step S204.

[0084] The above modules utilize multiple indicators to calculate node importance rankings, and then use these rankings to identify key nodes. This avoids the problem of biased key node identification caused by using a single indicator to assess node importance. The weights of multiple indicators are jointly determined by the analytic hierarchy process (AHP) and the entropy weight method, combining subjective and objective approaches to make the weight values ​​of each indicator more reasonable. This achieves the goal of comprehensively and reasonably assessing node importance, improves the accuracy of key node identification, and solves the problem of biased key node identification methods in related technologies.

[0085] As an optional embodiment, the second determining module includes: a first determining unit, used to determine the first weight corresponding to any indicator using the analytic hierarchy process; a second determining unit, used to determine the second weight corresponding to any indicator using the entropy weight method; and a third determining unit, used to determine the comprehensive weight corresponding to any indicator based on the arithmetic square root of the product of the first weight and the second weight corresponding to any indicator.

[0086] As an optional embodiment, the first determining unit includes: a first construction submodule, used to construct a hierarchical structure, wherein the target layer of the hierarchical structure is key node identification, the criterion layer of the hierarchical structure includes centrality indicators and importance indicators, the indicator layer of the hierarchical structure includes node connection number, node betweenness number, and proximity centrality, node connection number and node betweenness number are factor layers of centrality indicators, and proximity centrality is a factor layer of importance indicators; a second construction submodule, used to establish a judgment matrix corresponding to the hierarchical structure; and a first determining submodule, used to determine the first weight corresponding to any indicator based on the hierarchical single ranking and the hierarchical overall ranking.

[0087] As an optional embodiment, the second determining unit includes: a normalization submodule, used to normalize the index values ​​of all nodes corresponding to any index; a second determining submodule, used to determine the information value corresponding to any index based on the normalized index values ​​of all nodes corresponding to any index; and a third determining submodule, used to determine the second weight of any index based on the information values ​​of all indicators.

[0088] As an optional embodiment, the third determining module includes: an acquisition unit, used to acquire the node betweenness numbers of all nodes and the inter-node dependency between any two nodes, wherein the node betweenness number is the proportion of the number of shortest paths passing through the target node to the total number of shortest paths, and the inter-node dependency represents the degree of dependence of the target node on any non-target node; a calculation unit, used to calculate the node importance ranking value at multiple time points using an iterative algorithm based on multiple index values ​​of any node, multiple comprehensive weights corresponding to the multiple index values, a state transition matrix, a node importance ranking matrix, and a time factor, wherein the state transition matrix and the node importance ranking matrix are initialized based on the inter-node dependency between any two nodes and the node betweenness numbers of all nodes; and a comparison unit, used to take the node importance ranking value at the next time point as the node importance ranking value of any node when the difference between the node importance ranking value at the previous time point and the next time point is less than a preset standard value.

[0089] As an optional embodiment, the time factor is determined based on the ratio of the first product to the second product. The first product is the product of the joining time of any node and the damping factor, and the second product is the product of the sum of the joining times of all nodes and the number of connections of any node. The node joining time is the difference between the current time and the node joining time.

[0090] As an optional embodiment, the identification module includes: a sorting unit, used to sort the node importance ranking values ​​of all nodes from largest to smallest to obtain a sorting result; and a selection unit, used to select the nodes corresponding to the node importance ranking values ​​of the first preset positions in the sorting result as key nodes.

[0091] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of a device, can operate in situations such as... Figure 1 The hardware environment shown can be implemented through software or hardware, and the hardware environment includes the network environment.

[0092] According to another aspect of the present invention, an electronic device for implementing the above-described key node identification method is also provided. The electronic device may be a server, a terminal, or a combination thereof.

[0093] Figure 6 This is a structural block diagram of an optional electronic device according to an embodiment of the present invention, such as... Figure 6 As shown, the system includes a processor 601, a communication interface 602, a memory 603, and a communication bus 604. The processor 601, communication interface 602, and memory 603 communicate with each other via the communication bus 604. The memory 603 stores computer programs. When the processor 601 executes the computer program stored in the memory 603, it performs the following steps:

[0094] Multiple indicator values ​​are determined for any node based on multiple indicators, which are used to assess the importance of the node; the comprehensive weight corresponding to any indicator is determined using the analytic hierarchy process (AHP) and the entropy weight method; the node importance ranking value is determined based on the multiple indicator values ​​of any node and the comprehensive weight corresponding to the multiple indicators; key nodes are identified based on the node importance ranking values ​​of all nodes.

[0095] Optionally, in this embodiment, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0096] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0097] The memory may include RAM, or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0098] As an example, such as Figure 6 As shown, the memory 603 may include, but is not limited to, the first determining module 501, the second determining module 502, the third determining module 503, and the identifying module 504 in the critical node identification device. Furthermore, it may include, but is not limited to, other module units in the critical node identification device, which will not be elaborated upon in this example.

[0099] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0100] In addition, the aforementioned electronic device also includes a display for showing the results of key node identification.

[0101] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0102] Those skilled in the art will understand that Figure 6 The structure shown is for illustrative purposes only. The device implementing the above key node identification method can be a terminal device, such as a smartphone (e.g., Android phone, iOS phone), tablet computer, PDA, mobile Internet Devices (MID), PAD, etc. Figure 6 This does not limit the structure of the aforementioned electronic devices. For example, the terminal device may also include components that are more... Figure 6 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 6 The different configurations shown.

[0103] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, ROM, RAM, disk or optical disk, etc.

[0104] According to another aspect of the present invention, a storage medium is also provided. Optionally, in this embodiment, the storage medium can be used to execute program code for a key node identification method.

[0105] Optionally, in this embodiment, the storage medium may be located on at least one of the network devices in the network shown in the above embodiment.

[0106] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:

[0107] Multiple indicator values ​​are determined for any node based on multiple indicators, which are used to assess the importance of the node; the comprehensive weight corresponding to any indicator is determined using the analytic hierarchy process (AHP) and the entropy weight method; the node importance ranking value is determined based on the multiple indicator values ​​of any node and the comprehensive weight corresponding to the multiple indicators; key nodes are identified based on the node importance ranking values ​​of all nodes.

[0108] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated in this embodiment.

[0109] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0110] According to another aspect of the present invention, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the key node identification method in any of the above embodiments.

[0111] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0112] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the key node identification method of the various embodiments of the present invention.

[0113] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0114] In the several embodiments provided by this invention, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the solution provided in this embodiment, depending on actual needs.

[0116] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0117] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying key nodes, characterized in that, The method includes: Multiple indicator values ​​are determined for any node based on multiple indicators, which are used to assess the importance of the node; The comprehensive weight corresponding to any index is determined using the analytic hierarchy process and the entropy weight method. The importance ranking value of a node is determined based on multiple indicator values ​​of any given node and the comprehensive weight of those indicators. Identify key nodes based on their importance ranking across all nodes; The method of determining the comprehensive weight corresponding to any index using the analytic hierarchy process (AHP) and the entropy weight method includes: Use the analytic hierarchy process (AHP) to determine the first weight for any indicator. The second weight corresponding to any index is determined using the entropy weight method; The overall weight of any indicator is determined by the square root of the product of its first and second weights.

2. The key node identification method according to claim 1, characterized in that, The method of determining the first weight corresponding to any indicator using the analytic hierarchy process includes: A hierarchical structure is constructed, wherein the target layer of the hierarchical structure is key node identification, the criterion layer of the hierarchical structure includes centrality indicators and importance indicators, the indicator layer of the hierarchical structure includes node connection number, node betweenness number, and proximity centrality, the node connection number and node betweenness number are the factor layer of the centrality indicator, and the proximity centrality is the factor layer of the importance indicator. Establish the judgment matrix corresponding to the aforementioned hierarchical structure; The first weight corresponding to any indicator is determined based on the hierarchical single ranking and the hierarchical overall ranking.

3. The key node identification method according to claim 1, characterized in that, The method of determining the second weight corresponding to any index using the entropy weight method includes: Normalize the index values ​​of all nodes corresponding to any given index. The information value corresponding to any indicator is determined based on the indicator values ​​of all nodes corresponding to any normalized indicator. The second weight of any indicator is determined based on the information values ​​of all indicators.

4. The key node identification method according to claim 1, characterized in that, The step of determining the node importance ranking value based on multiple indicator values ​​of any node and the comprehensive weights corresponding to the multiple indicators includes: Obtain the node betweenness number of all nodes and the node dependency between any two nodes. The node betweenness number is the proportion of the number of shortest paths that pass through the target node to the total number of shortest paths. The node dependency represents the degree to which the target node depends on any non-target node. Based on multiple indicator values ​​of any node, multiple comprehensive weights corresponding to the multiple indicator values, state transition matrix, node importance ranking matrix, and time factor, an iterative algorithm is used to calculate the node importance ranking value at multiple time points. The state transition matrix and node importance ranking matrix are initialized based on the node dependency between any two nodes and the node betweenness of all nodes, respectively. If the difference between the node importance ranking value at the previous time and the next time is less than a preset standard value, the node importance ranking value at the next time is used as the node importance ranking value of any given node.

5. The key node identification method according to claim 4, characterized in that, The time factor is determined based on the ratio of the first product to the second product. The first product is the product of the time any node joins and the damping factor. The second product is the product of the sum of the times all nodes join and the number of connections to any node. The node join time is the difference between the current time and the time the node joins.

6. The key node identification method according to claim 1, characterized in that, The process of identifying key nodes based on the node importance ranking values ​​of all nodes includes: Sort all nodes by their importance ranking values ​​from largest to smallest to obtain the sorting result; The nodes corresponding to the importance ranking values ​​of the nodes in the first preset position in the sorting results are selected as key nodes.

7. A key node identification device, characterized in that, The device includes: The first determining module is used to determine multiple indicator values ​​for any node based on multiple indicators, wherein the multiple indicators are used to evaluate the importance of the node. The second determination module is used to determine the comprehensive weight corresponding to any indicator using the analytic hierarchy process and the entropy weight method. The third determining module is used to determine the importance ranking value of any node based on multiple indicator values ​​of the node and the comprehensive weights corresponding to the multiple indicators. The identification module is used to identify key nodes based on the node importance ranking value of all nodes; The second determining module includes: The first determining unit is used to determine the first weight corresponding to any indicator using the analytic hierarchy process. The second determining unit is used to determine the second weight corresponding to any index using the entropy weight method; The third determining unit is used to determine the comprehensive weight corresponding to any indicator based on the arithmetic square root of the product of the first weight and the second weight corresponding to any indicator.

8. The key node identification device according to claim 7, characterized in that, The first determining unit includes: The first construction submodule is used to construct a hierarchical structure. The target layer of the hierarchical structure is key node identification. The criterion layer of the hierarchical structure includes centrality indicators and importance indicators. The indicator layer of the hierarchical structure includes the number of node connections, the number of node betweennesses, and the proximity centrality. The number of node connections and the number of node betweennesses are the factor layers of the centrality indicators, and the proximity centrality is the factor layer of the importance indicators. The second construction submodule is used to establish the judgment matrix corresponding to the ladder hierarchy structure; The first determination submodule is used to determine the first weight corresponding to any indicator based on the hierarchical single sort and the hierarchical overall sort.

9. The key node identification device according to claim 7, characterized in that, The second determining unit includes: The normalization submodule is used to normalize the index values ​​of all nodes corresponding to any index. The second determination submodule is used to determine the information value corresponding to any indicator based on the indicator values ​​of all nodes corresponding to any normalized indicator. The third determination submodule is used to determine the second weight of any indicator based on the information values ​​of all indicators.

10. The key node identification device according to claim 7, characterized in that, The third determining module includes: The acquisition unit is used to acquire the node betweenness of all nodes and the node dependency between any two nodes. The node betweenness is the proportion of the number of shortest paths that pass through the target node to the total number of shortest paths. The node dependency represents the degree of dependence of the target node on any non-target node. The calculation unit is used to calculate the node importance ranking value at multiple time points using an iterative algorithm based on multiple indicator values ​​of any node, multiple comprehensive weights corresponding to the multiple indicator values, state transition matrix, node importance ranking matrix and time factor. The state transition matrix and node importance ranking matrix are initialized based on the node dependency between any two nodes and the node betweenness of all nodes, respectively. The comparison unit is used to take the node importance ranking value of the next moment as the node importance ranking value of any node when the difference between the node importance ranking values ​​of the previous moment and the next moment is less than a preset standard value.

11. The key node identification device according to claim 10, characterized in that, The time factor is determined based on the ratio of the first product to the second product. The first product is the product of the time any node joins and the damping factor. The second product is the product of the sum of the times all nodes join and the number of connections to any node. The node join time is the difference between the current time and the time the node joins.

12. The key node identification device according to claim 7, characterized in that, The identification module includes: The sorting unit is used to sort all nodes by their importance ranking values ​​from largest to smallest to obtain the sorting result; The selection unit is used to select the nodes corresponding to the node importance ranking values ​​of the first preset positions in the sorting results as key nodes.

13. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein, The processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that... The memory is used to store computer programs; The processor is configured to execute the critical node identification method according to any one of claims 1 to 6 by running the computer program stored in the memory.

14. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the key node identification method according to any one of claims 1 to 6.

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