Data processing method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring a network topological graph; according to a first parameter and a second parameter, a neighbor aggregation index of the node is determined, and the first parameter represents a summation result of degree centrality of the node and degree centrality of all adjacent nodes of the node; the second parameter represents a product result of an attenuation function of the clustering coefficients of the nodes and a target parameter, and the target parameter is the sum of the clustering coefficients of all adjacent nodes of the nodes; fusing the neighborhood gathering index with the information entropy to obtain a neighborhood gathering entropy index of the node; and according to the neighbor gathering entropy index, determining an importance sequence of the nodes. According to the node importance evaluation method and device, the technical problem that the node importance evaluation precision is insufficient due to the fact that the related node importance evaluation method cannot reflect the influence of the adjacent nodes on the target node and the effect of the target node in the whole network is solved.

Description

Technical Field

[0001] The present application relates to the technical field of complex network analysis, and more specifically, to a data processing method and device, a non-volatile storage medium, and an electronic device. Background Art

[0002] With the rapid development of information technology and the rise of data science, complex network theory is playing an increasingly important role in diverse fields, such as social networks, bioinformatics, neuroscience, and information technology security. As a vast system composed of numerous nodes (representing entities or individuals) interconnected by edges (representing relationships or interactions between nodes), complex networks' internal structural characteristics and the assessment of node importance have become hot topics and challenges in research.

[0003] In related complex network analysis methods, node importance assessment relies on classic centrality metrics, such as degree centrality, betweenness centrality, and closeness centrality. While these methods can reflect a node's position and influence in the network to a certain extent, they can only reflect the local characteristics of a single node. They cannot reflect the influence of surrounding nodes on the node or the role of the node in the entire network, resulting in low accuracy in node importance assessment.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The present application provides a data processing method and device, a non-volatile storage medium, and an electronic device to at least solve the technical problem of insufficient accuracy in node importance assessment caused by the inability of relevant node importance assessment methods to reflect the impact of adjacent nodes on target nodes and the role of target nodes in the overall network.

[0006] According to one aspect of the present application, a data processing method is provided, comprising: obtaining a network topology graph, wherein the network topology graph includes a plurality of nodes; determining a neighboring index of the node based on a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of an attenuation function of the node's clustering coefficient and a target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighboring index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes; the neighboring index of the node is fused with the information entropy of the node to obtain a neighboring entropy index of the node; and according to the neighboring entropy index of the node, determining the importance ranking of the nodes in the network topology graph.

[0007] Optionally, the second parameter is calculated by the following formula: c (i) = e-C(i) ∑ j∈Γ(i) C(j) where I c (i) is the second parameter of the i-th node, e -C(i) is the attenuation function of the clustering coefficient of the i-th node, C(i) is the clustering coefficient of the i-th node, Γ(i) is the set of adjacent nodes of the i-th node, i is a positive integer not greater than N, and N is the number of nodes in the network topology graph.

[0008] Optionally, the neighborhood index of the node is fused with the information entropy of the node to obtain the neighborhood entropy index of the node, including: normalizing the neighborhood index by the following formula to obtain the normalized neighborhood index: Where cn(i) is the neighborhood index of the i-th node, cn(j) is the neighborhood index of the j-th node, j is a positive integer from 1 to N, and CN(i) is the normalized neighborhood index of the i-th node. The neighborhood entropy index of the node is calculated using the following formula: Among them, NACE(G) is the neighborhood entropy index of the node.

[0009] Optionally, the importance ranking of nodes in the network topology graph is determined based on the neighborhood entropy index of the node, including: determining the neighborhood entropy centrality index of the target node as the difference between the neighborhood entropy index of the node and the neighborhood entropy index of the first node, wherein the first node is all nodes in the network topology graph except the target node, and the target node is any node in the network topology graph; sorting the values of the neighborhood entropy centrality index of the nodes in a preset order to obtain the importance ranking of the nodes in the network topology graph.

[0010] Optionally, after obtaining the network topology graph, the method also includes: obtaining a relationship strength index between a node in the network topology graph and its adjacent nodes; when the relationship strength index is greater than a preset threshold, determining the edge weight between the node and the adjacent node to be 1; when the relationship strength index is not greater than the preset threshold, determining the edge weight between the node and the adjacent node to be 0; constructing an adjacency matrix for representing the network topology graph, wherein the rows of the adjacency matrix are used to represent each node in the network topology graph, and the columns of the adjacency matrix are used to represent the edge weights corresponding to the nodes.

[0011] Optionally, the degree centrality of a node is determined based on the number of edges directly connected to the node.

[0012] Optionally, the clustering coefficient of a node is determined according to the ratio of the actual number of edges between the node and its neighboring nodes to the maximum possible number of edges.

[0013] According to another aspect of the present application, a data processing device is also provided, including: an acquisition module for acquiring a network topology graph, wherein the network topology graph includes multiple nodes; a first determination module for determining the neighborhood index of the node based on a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of an attenuation function of the node's clustering coefficient and a target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighborhood index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes; a fusion module for fusing the node's neighborhood index with the node's information entropy to obtain the node's neighborhood entropy index; and a second determination module for determining the importance ranking of the nodes in the network topology graph based on the node's neighborhood entropy index.

[0014] According to another aspect of the present application, a non-volatile storage medium is provided, which includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above data processing method.

[0015] According to another aspect of the present application, an electronic device is provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the above data processing method is executed when the program is run.

[0016] According to yet another aspect of the present application, a computer program is provided, wherein the above data processing method is implemented when the computer program is executed by a processor.

[0017] According to another aspect of the present application, a computer program product is provided, which includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above data processing method is implemented.

[0018] In the present application, a network topology graph is obtained, wherein the network topology graph includes multiple nodes; the neighborhood index of the node is determined according to a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of the attenuation function of the node's clustering coefficient and the target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighborhood index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes; the neighborhood index of the node is fused with the information entropy of the node to obtain the neighborhood entropy index of the node; according to the neighborhood entropy index of the node, a method for ranking the importance of nodes in the network topology graph is determined, thereby achieving the purpose of comprehensively reflecting the influence of neighboring nodes on the target node and the role of the target node in the overall network, thereby achieving the technical effect of improving the accuracy of node importance assessment, and then solving the technical problem of insufficient node importance assessment accuracy caused by the inability of relevant node importance assessment methods to reflect the influence of neighboring nodes on the target node and the role of the target node in the overall network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application;

[0021] Figure 2 is a correlation line graph of node importance ranking according to an embodiment of the present application;

[0022] Figure 3 is a structural diagram of a data processing device according to an embodiment of the present application;

[0023] Figure 4 This is a hardware structure block diagram of a computer terminal according to a data processing method of an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present application, a method embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Acquire a network topology map, where the network topology map includes multiple nodes.

[0029] In step S102, a topological graph of a complex network is obtained by reading a file, accessing a database, or using a web crawler. The network topological graph includes multiple nodes, each representing an entity or individual in the network. Nodes are connected by edges, representing interactions or relationships between entities. The network can be a social network, a biological network, an information network, or the like.

[0030] In social networks, entities usually refer to users or individuals.

[0031] Biological networks primarily include protein interaction networks, gene regulatory networks, and ecological food webs. In protein interaction networks, nodes can represent proteins, and edges represent physical or functional interactions between proteins. In gene regulatory networks, nodes can be genes or transcription factors, and edges represent regulatory relationships. In ecological food webs, nodes can be species, and edges represent predator-prey relationships within the food chain.

[0032] Information networks include the Internet, the World Wide Web, citation networks, etc. In a network, nodes can be web pages, documents, computer devices, etc., and edges represent links, citations, or other forms of data flow.

[0033] Step S102 can define a complex network in the form of a graph in graph theory, and represent the complex network with nodes and edges, specifically: G = (v, ε). Where v = {1, 2, 3, 4, ..., N} represents N nodes, Represents an edge set.

[0034] According to some optional embodiments of the present application, after obtaining the network topology map, the following steps can also be performed: obtain the relationship strength index between the nodes in the network topology map and the adjacent nodes of the nodes; when the relationship strength index is greater than a preset threshold, determine the edge weight between the node and the adjacent node to be 1; when the relationship strength index is not greater than the preset threshold, determine the edge weight between the node and the adjacent node to be 0; construct an adjacency matrix for representing the network topology map, wherein the rows of the adjacency matrix are used to represent each node in the network topology map, and the columns of the adjacency matrix are used to represent the edge weights corresponding to the nodes.

[0035] It is understandable that the situation of undirected complex network is mainly considered in this embodiment. The complex network read in life contains edge weights. In order to understand the structure and association of the complex network more clearly, it is necessary to determine the threshold value of the network edge weight and binarize the complex network. Specifically, when the relationship strength between nodes is greater than the threshold value, the edge weight is set to 1, otherwise it is 0. Among them, a reasonable threshold value is very important, and it is possible to filter out the edges with weaker connectivity in the network. Reasonable thresholding can reduce the complexity of the network and better distinguish network areas with larger weight differences. Finally, according to the result after thresholding, the above-mentioned adjacency matrix is constructed.

[0036] In biological networks, such as protein interaction networks, relationship strength can be the affinity or binding strength between proteins, or the efficiency and strength of gene regulation in gene regulatory networks.

[0037] In information networks, such as the hyperlink network between web pages or the academic citation network, the strength of a relationship can be measured by the weight of the link or the number of citations. On the Internet, if a web page is linked to by a large number of other pages, it can be considered that the strength of its relationship with these pages is high.

[0038] When nodes represent computing devices, the strength of relationships between them can be determined by a variety of factors, primarily related to network communication and data exchange. In the context of information networks or computer networks, definitions of inter-node relationship strength may include the following: 1. Communication frequency: Frequent data transmission or communication between two computing devices increases the strength of their relationship. For example, frequent requests and responses between a server and a client, or frequent file transfers between two computers. 2. Data volume: The amount of data exchanged between nodes can also reflect relationship strength. Large amounts of data exchanged, such as data transfer between servers within a data center, may indicate closer collaboration or dependency. 3. Bandwidth utilization: The frequency of use of the communication link, or bandwidth utilization. If the connection between two nodes is frequently fully utilized, this may indicate a high strength of relationship between them. 4. Latency and response time: The latency and response time of inter-node communication can reflect the connectivity and closeness of the connection. Lower latency generally indicates a more direct or higher-quality connection, thus increasing relationship strength.

[0039] Step S104: Determine the neighboring index of the node based on the first parameter and the second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of the attenuation function of the node's clustering coefficient and the target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighboring index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes.

[0040] The first parameter focuses on the node's degree centrality (i.e., the number of directly connected edges to the node) and the sum of the degree centralities of all of its neighbors. This represents the connectivity and influence of a node and its surroundings in the network. A high degree centrality means that a node has more direct connections in the network, and the sum of the degree centralities of its neighbors further reinforces this, reflecting that not only is the node itself well-connected, but its neighbors also have a strong network presence.

[0041] The second parameter focuses on the clustering coefficient of the node and the clustering coefficient of the adjacent nodes. The clustering coefficient measures the density of the connections between the node and its adjacent nodes. A high clustering coefficient means that a close community is formed between the node and its adjacent nodes, and information or effects tend to encounter fewer obstacles when propagating within such a community. The second parameter introduces a decay function to weight the sum of the clustering coefficient of the node and the clustering coefficient of the adjacent nodes, which means that a tighter community (i.e., a high clustering coefficient) will make a greater contribution to this parameter. At the same time, taking into account the clustering coefficient of the adjacent nodes can reflect the structural stability of the local network in which the node is located and its ability to promote information diffusion.

[0042] The neighborhood clustering index combines the two aforementioned parameters to comprehensively reflect the local network characteristics of a node. It considers both the node's direct influence (reflected by degree centrality) and the structural closeness and information dissemination efficiency of the node's local network (reflected by the weighted clustering coefficient). This index is designed on the principle that a truly important node in a network not only possesses extensive direct connections but also forms a highly concentrated community around it, which facilitates the rapid dissemination of information and the amplification of its effects. The neighborhood clustering index provides a deeper understanding of the comprehensive influence and critical role of nodes in complex networks.

[0043] Preferably, the degree centrality of a node is determined based on the number of edges directly connected to the node. It is understood that degree centrality measures the degree to which a node is directly connected to other nodes in the network, which is simply the number of connections or adjacent nodes of a node.

[0044] Preferably, the clustering coefficient of a node is determined based on the ratio of the actual number of edges between the node and its neighboring nodes to the maximum possible number of edges. It is understood that the clustering coefficient reflects the degree of clustering of the node's surroundings, that is, the density of interconnections between the node's neighboring nodes. This helps determine whether the nodes form a close community or group in the local environment. The clustering coefficient is calculated based on the ratio of the actual number of edges between a node's neighboring nodes to the theoretically maximum possible number of edges.

[0045] According to some other optional embodiments of the present application, the second parameter may be calculated using the following formula:

[0046]

[0047] Among them, I c (i) is the second parameter of the i-th node, e -C(i) is the attenuation function of the clustering coefficient of the i-th node. The smaller the clustering coefficient C(i) of node i is, the smaller the clustering coefficient C(i) of node i is. -C(i) The closer it is to 1, the better the effect of C(i) on I c (i) is smaller, and vice versa. c (i) has a greater impact; C(i) is the clustering coefficient of the i-th node, Γ(i) is the set of adjacent nodes of the i-th node, i is a positive integer not greater than N, and N is the number of nodes in the network topology graph.

[0048] It is worth noting that the second parameter further reveals the complex relationship between the local connectivity characteristics of nodes in complex networks and their propagation capabilities. Its definition takes into account two aspects: first, the clustering coefficient of the target node and the clustering coefficients of its adjacent nodes. Second, the mechanism by which the clustering coefficient of the target node and the clustering coefficients of its adjacent nodes influence propagation capabilities is considered. Specifically, relevant research has shown that the clustering coefficient of a node is negatively correlated with propagation capabilities: as the clustering coefficient of a node increases, its influence decreases accordingly. Therefore, the exponential decay of the node clustering coefficient is taken into account. The clustering coefficient of a node's adjacent nodes, on the other hand, exhibits a positive effect on propagation capabilities, meaning that a higher clustering coefficient of adjacent nodes can enhance the influence of that node. Therefore, the sum of the clustering coefficients of adjacent nodes is taken into account.

[0049] The first parameter can also be calculated using the following formula:

[0050]

[0051] Among them, I D (i) is the first parameter of the i-th node, C D (i) is the degree centrality of the i-th node.

[0052] Furthermore, the neighboring index is calculated by the following formula:

[0053] cn(i)=I D (i)I C (i)

[0054] Among them, cn(i) is the neighborhood index of the i-th basis point.

[0055] Step S106: The neighborhood index of the node is integrated with the information entropy of the node to obtain the neighborhood entropy index of the node.

[0056] The above-mentioned step S106, which fuses the node's neighborhood index with the information entropy to obtain the neighborhood entropy index, is an innovative method for evaluating the importance of nodes in complex networks. This method combines the local structural properties of the node (reflected by the neighborhood index) and the uncertainty or information content of the node in the overall network (measured by the information entropy), thereby providing a comprehensive perspective to understand the role and value of the node in the network. Among them, the neighborhood index combines the degree centrality of the node and the clustering coefficient with its neighboring nodes, reflecting not only the connection strength of a single node, but also the degree of clustering of the local network in which the node is located. This composite index can more accurately capture the influence and role of the node in the local structure. Information entropy is an indicator that measures the uncertainty or randomness of information in an information source from the perspective of information theory. In a complex network, the information entropy of a node expresses the uncertainty of the node's connection pattern or state in the network. The more complex and unpredictable the node's connection, the higher its information entropy value, which reflects that the node may assume more information transmission or control functions in the network.

[0057] In some optional embodiments of the present application, step S106 may be implemented by the following method:

[0058] The neighboring index is normalized by the following formula to obtain the normalized neighboring index:

[0059] Where cn(i) is the neighborhood index of the i-th node, cn(j) is the neighborhood index of the j-th node, j is a positive integer from 1 to N, and CN(i) is the normalized neighborhood index of the i-th node.

[0060] Furthermore, the node neighborhood entropy index is calculated using the following formula:

[0061]

[0062] Among them, NACE(G) is the neighborhood entropy index of the node.

[0063] Step S108: determining the importance ranking of nodes in the network topology graph according to the neighborhood entropy index of the nodes.

[0064] In some optional embodiments of the present application, step S108 can be implemented by the following method: the difference between the neighborhood entropy index of the node and the neighborhood entropy index of the first node is determined as the neighborhood entropy centrality index of the target node, wherein the first node is all nodes in the network topology graph except the target node, and the target node is any node in the network topology graph; the values of the neighborhood entropy centrality indicators of the nodes are sorted in a preset order to obtain the importance ranking of the nodes in the network topology graph.

[0065] It can be understood that the above NACE(G) represents the entropy value of the node neighborhood entropy of the complex network G. The entropy centrality of node i can be defined as the change in node neighborhood entropy when the node and its connected edges are removed from the network. Using NACEC(i) to represent the node neighborhood entropy centrality of node i, we can get:

[0066] NACEC(i)=NACE(G)-NACE(G / i)

[0067] Among them, NACE(G) represents the entropy value of the neighborhood entropy of the complex network node G, and NACE(G / i) represents the neighborhood entropy of the complex network node after removing node i.

[0068] Furthermore, the neighborhood entropy index values of all nodes in the network are collected in a list or array. The importance of a node is proportional to the neighborhood entropy index value, that is, the node with a larger neighborhood entropy index value is more important. Therefore, the node with the largest neighborhood entropy index value can be sorted in descending order, and the node list with the largest neighborhood entropy index value is ranked first. Use the sorting function provided in the programming language to sort the node list according to the neighborhood entropy index value, and ensure that the sorting operation is performed in order from large to small (descending order). After the sorting is completed, check the results to ensure that there are no errors, such as confirming that the node with the largest neighborhood entropy index value is indeed ranked first, and so on. Based on the sorting results, the most critical nodes in the network can be easily identified, that is, those nodes with the highest neighborhood entropy index value that play a core role in network structure and information transmission.

[0069] The above steps S102 to S108 are adopted to obtain a network topology map, wherein the network topology map includes multiple nodes; according to the first parameter and the second parameter, the neighborhood index of the node is determined, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the adjacent nodes of the node; the second parameter represents the product of the attenuation function of the node's clustering coefficient and the target parameter, and the target parameter is the sum of the clustering coefficients of all the adjacent nodes of the node; the neighborhood index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the adjacent nodes of the node; the neighborhood index of the node is fused with the information entropy of the node to obtain the neighborhood entropy index of the node; according to the neighborhood entropy index of the node, the method of ranking the importance of the nodes in the network topology map is determined, thereby achieving the purpose of comprehensively reflecting the influence of the adjacent nodes on the target node and the role of the target node in the overall network, thereby achieving the technical effect of improving the accuracy of the evaluation of the importance of the node.

[0070] The following is a combination of Experiment 1 and Experiment 2. Figure 1 The effectiveness of the steps shown is verified.

[0071] Experiment 1: Using accuracy metrics to validate in yeast-protein interaction networks Figure 1 Validity of the steps shown.

[0072] The yeast-protein interaction network of Saccharomyces cerevisiae is a complex network model commonly used to validate algorithm effectiveness. Nodes in the network represent proteins, while each edge represents an interaction between two proteins. The yeast-protein interaction network contains 5,093 nodes and 24,743 edges. Of the 5,093 nodes in the yeast network, 1,167 are considered important, 3,591 are not important, and the importance of the remaining 335 nodes is unknown.

[0073] This example uses a variety of centrality metrics to identify important proteins in this yeast network. The accuracy of identifying important proteins is assessed using a precision metric. This metric only considers whether the top k nodes are accurately predicted. Its value is equal to the proportion of accurately predicted nodes among the top k nodes. The precision metric is defined as follows:

[0074]

[0075] Among them, n p Indicates the number of important nodes included in the top k predicted nodes. In this yeast network test, k is set to 1167.

[0076] As can be seen from Table 1, the accuracy of the Node Adjacent Clustering Entropy Centrality (NACEC) algorithm proposed in this application is comparable to other important metrics. The NACEC algorithm can accurately identify important nodes in the network. The results show that using the NACEC algorithm as a metric for evaluating node importance is reasonable and sufficient.

[0077] Table 1 Accuracy metrics of various algorithms in yeast networks

[0078]

[0079] Experiment 2: Use the SIR model to verify the effectiveness of the algorithm.

[0080] The Susceptible, Infectious, Recovered (SIR) model is a common epidemiological model used to describe the spread of infectious diseases in a population. It divides the population into three categories: susceptible, infected, and recovered. Susceptible individuals are those who have not yet contracted the disease but are at risk of infection. They may become infected through contact with an infected person. Infected individuals are those who have already contracted the disease and have the potential to spread it to others. They can then transmit the disease to susceptible individuals, causing them to become newly infected. Recovered individuals are those who have recovered from the infection and are immune; they are no longer susceptible to the disease and will not be infected again for a period of time.

[0081] At the beginning of the experiment, every node in the network is susceptible. Except for the original infected node, each infected node will infect its neighboring nodes with a probability of β. After infection, each infected node will recover with a probability of γ and become a recovered node. Once recovered, it will no longer be infected. When there are no infected nodes left in the network, the spread ends.

[0082] The network epidemic threshold is an important concept in the SIR model, which represents the minimum condition required for the spread of infectious diseases in the network. The calculation formula is β th = <k> / (<k 2 >- <k>), where k represents the average degree, k 2 Since the iteration of the SIR model is random, in this embodiment, 5000 simulation iterations are performed for a network with less than 100 nodes, and 1000 simulation iterations are performed for a network with more than 100 nodes.

[0083] To better measure the performance of the algorithm proposed in this application, the Kendall Tau coefficient τ is used to measure the correlation between the node importance rankings obtained by different algorithms and the node importance rankings simulated by the SIR model. The Kendall Tau correlation coefficient τ is a statistical method used to measure the correlation between two variables. It is used to measure the degree of consistency between the ranks of two variables. Specifically, X represents the node importance ranking simulated by the SIR model, and Y represents the node importance ranking obtained by other algorithms.

[0084] For each pair of observations (X(i), Y(i)), calculate whether it is paired with the other observations (X(j), Y(j)). If X(i)>X(j) and Y(i)>Y(j), or X(i)<X(j) and Y(i)<Y(j), then the two groups of observations are considered paired. If X(i)>X(j) and Y(i)<Y(j), or X(i)<X(j) and Y(i)>Y(j), then the two groups of observations are considered unpaired. If X(i)=X(j) and Y(i)=Y(j), then the two groups of observations are also considered unpaired. During the calculation, record the number of paired observations C and the number of unpaired observations D.

[0085] The Kendall Tau correlation coefficient τ can be considered as the ratio of the number of paired pairs C minus the number of unpaired pairs D. The calculation formula is as follows:

[0086]

[0087] Where N is the total number of nodes in the network.

[0088] The Kendall Tau correlation coefficient τ ranges from -1 to 1. When the Kendall Tau correlation coefficient τ is closer to 1, it means that the algorithm ranking result is closer to the node importance ranking simulated by the SIR model.

[0089] In this experiment, the proposed NACEC algorithm was compared with other common node importance algorithms, including K-shell, DC, BC, CC, PE, H-index, GIN, and LGC. The following table shows the complex networks used in this experiment. These complex networks are from a public dataset. These networks are already unweighted and undirected, eliminating the need to construct an adjacency matrix to represent the network topology. These networks are: the Ca_Sandi_Auth network, the Netscience network, and the Polbooks network. Table 2 shows information about these networks.

[0090] Table 2. Relevant information of Ca_Sandi_Auth network, Netscience network, and Polbooks network

[0091]

[0092] like Figure 2 The figure shows the correlation between node importance rankings calculated by different algorithms in three real networks and the rankings obtained from the SIR model by adjusting the infection probability β. In the Ca_Sandi_Auth and Polbooks network experiments, the Kendall Tau correlation coefficient τ calculated by the NACEC algorithm was generally the highest, indicating that it was closest to the node importance ranking simulated by the SIR model and exhibited the best performance. In the Netscience network, the NACEC algorithm maintained the best performance, except for the infection probability β range of 0.8-1, where it performed poorly.

[0093] In Table 3, the network popularity threshold β is calculated th From the Kendall Tau correlation coefficient τ, we can see that the correlation coefficients calculated by the NACEC algorithm are all above 0.8, and it shows the highest similarity among the three networks. The NACEC algorithm performs better than other traditional algorithms.

[0094] The above two sets of experimental results show that the NACEC algorithm proposed in this application can accurately identify key nodes in the network and has strong applicability, showing good experimental results and algorithm performance in different networks.

[0095] Table 3 Kendall Tau correlation coefficient τ of various algorithms under different network popularity thresholds

[0096]

[0097] In this application, multiple groups of commonly used node importance algorithm performance evaluation indicators are used to verify the effectiveness of the algorithm, including precision measurement and SIR model verification algorithm effectiveness. In the experiment of using precision measurement to verify the effectiveness of the algorithm in the yeast-protein interaction network, compared with a variety of traditional algorithms, the NACEC algorithm showed the best effect in predicting important nodes in the yeast-protein interaction network. In the SIR model verification algorithm effectiveness experiment, the important nodes calculated by the NACEC algorithm were most similar to the node importance results simulated by the SIR model, proving that the nodes calculated by it are closer to the node importance ranking results simulated by the SIR model than those calculated by other algorithms. At the same time, the correlation coefficient under the network popularity threshold was calculated, and the NACEC algorithm showed the best level in all three networks.

[0098] In summary, the concept of node neighbor clustering proposed in this application comprehensively considers the clustering coefficient and degree centrality of a node and its adjacent nodes, and conducts an in-depth analysis of the local structural characteristics of a complex network from multiple perspectives. This multi-angle evaluation method enables the algorithm to more accurately reflect the influence and importance of a node in the network. In addition, this application proposes the NACEC algorithm, which not only focuses on the local characteristics of a single node, but also combines the local and global characteristics of a node by introducing the concept of information entropy, thereby achieving a comprehensive analysis of the node.

[0099] Figure 3 is a structural diagram of a data processing device according to an embodiment of the present application, such as Figure 3 As shown, the device includes:

[0100] The acquisition module 31 is configured to acquire a network topology map, wherein the network topology map includes a plurality of nodes.

[0101] The first determination module 32 is used to determine the neighboring index of the node based on the first parameter and the second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of the attenuation function of the node's clustering coefficient and the target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighboring index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes.

[0102] The fusion module 33 is used to fuse the neighborhood index of a node with the information entropy of the node to obtain the neighborhood entropy index of the node.

[0103] The second determining module 34 is configured to determine the importance ranking of nodes in the network topology graph according to the neighborhood entropy index of the nodes.

[0104] Optionally, the second parameter is calculated by the following formula: c (i) = e -C(i) ∑ j∈Γ(i) C(j) where I c (i) is the second parameter of the i-th node, e -C(i) is the attenuation function of the clustering coefficient of the i-th node, C(i) is the clustering coefficient of the i-th node, Γ(i) is the set of adjacent nodes of the i-th node, i is a positive integer not greater than N, and N is the number of nodes in the network topology graph.

[0105] Optionally, the neighborhood index of the node is fused with the information entropy of the node to obtain the neighborhood entropy index of the node, including the following steps: normalizing the neighborhood index by the following formula to obtain the normalized neighborhood index: Where cn(i) is the neighborhood index of the i-th node, cn(j) is the neighborhood index of the j-th node, j is a positive integer from 1 to N, and CN(i) is the normalized neighborhood index of the i-th node. The neighborhood entropy index of the node is calculated using the following formula: Among them, NACE(G) is the neighborhood entropy index of the node.

[0106] Optionally, the importance ranking of nodes in the network topology graph is determined based on the neighborhood entropy index of the node, including the following steps: the neighborhood entropy centrality index of the target node is determined as the difference between the neighborhood entropy index of the node and the neighborhood entropy index of the first node, wherein the first node is all nodes in the network topology graph except the target node, and the target node is any node in the network topology graph; the values of the neighborhood entropy centrality index of the nodes are sorted in a preset order to obtain the importance ranking of the nodes in the network topology graph.

[0107] Optionally, after obtaining the network topology map, the following steps can also be performed: obtain the relationship strength index between the node in the network topology map and the adjacent nodes of the node; when the relationship strength index is greater than a preset threshold, determine the edge weight between the node and the adjacent node to be 1; when the relationship strength index is not greater than the preset threshold, determine the edge weight between the node and the adjacent node to be 0; construct an adjacency matrix for representing the network topology map, wherein the rows of the adjacency matrix are used to represent each node in the network topology map, and the columns of the adjacency matrix are used to represent the edge weights corresponding to the nodes.

[0108] Optionally, the degree centrality of a node is determined based on the number of edges directly connected to the node.

[0109] Optionally, the clustering coefficient of a node is determined according to the ratio of the actual number of edges between the node and its neighboring nodes to the maximum possible number of edges.

[0110] It should be noted that the above Figure 3 The modules in the embodiment can be program modules (for example, a set of program instructions that implement a specific function) or hardware modules. For the latter, they can be expressed in the following forms, but are not limited to these: the expression form of each of the above modules is a processor, or the functions of each of the above modules are implemented by a processor.

[0111] It should be noted that Figure 3 The preferred implementation of the embodiment shown can be found in Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0112] Figure 4 FIG1 shows a hardware structure block diagram of a computer terminal for implementing a data processing method. Figure 4 As shown, the computer terminal 40 may include one or more (402a, 402b, ..., 402n are shown in the figure) processors 402 (the processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 404 for storing data, and a transmission module 406 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components than shown, or with Figure 4 Different configurations shown.

[0113] It should be noted that the one or more processors 402 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computer terminal 40. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0114] The memory 404 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the data processing method in the embodiment of the present application. The processor 402 executes various functional applications and data processing by running the software programs and modules stored in the memory 404, that is, implementing the above-mentioned data processing method. The memory 404 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 404 may further include a memory remotely located relative to the processor 402, and these remote memories may be connected to the computer terminal 40 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0115] The transmission module 406 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 40. In one embodiment, the transmission module 406 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission module 406 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0116] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 40 .

[0117] It should be noted that, in some optional embodiments, the above Figure 4 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. Figure 4 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0118] It should be noted that Figure 4 The computer terminal shown is used to execute Figure 1 The data processing method shown, therefore the relevant explanations in the execution method of the above command are also applicable to the electronic device and will not be repeated here.

[0119] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the above data processing method.

[0120] A program for a non-volatile storage medium to perform the following functions: obtaining a network topology diagram, wherein the network topology diagram includes a plurality of nodes; determining a neighboring index of the node based on a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of a decay function of the node's clustering coefficient and a target parameter, wherein the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighboring index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes; the neighboring index of the node is fused with the information entropy of the node to obtain a neighboring entropy index of the node; and according to the neighboring entropy index of the node, determining the importance ranking of the nodes in the network topology diagram.

[0121] An embodiment of the present application further provides an electronic device, comprising: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the above data processing method is executed when the program is run.

[0122] The processor is used to run a program that performs the following functions: obtaining a network topology diagram, wherein the network topology diagram includes multiple nodes; determining a neighboring index of the node based on a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of an attenuation function of the node's clustering coefficient and a target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighboring index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all the node's neighboring nodes; the neighboring index of the node is integrated with the information entropy of the node to obtain a neighboring entropy index of the node; and according to the neighboring entropy index of the node, determining the importance ranking of the nodes in the network topology diagram.

[0123] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0124] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0125] In the above-mentioned embodiments of the present application, the collected information is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary protection measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0127] The units described as separate components may or may not be physically separate, and 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 units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0128] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0130] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.< / k> < / k>

Claims

1. A data processing method, characterized in that: include: Acquire a network topology graph, wherein the network topology graph includes a plurality of nodes; Determine a neighboring index of the node according to a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all the node's neighboring nodes; the second parameter represents the product of an attenuation function of the node's clustering coefficient and a target parameter, and the target parameter is the sum of the clustering coefficients of all the node's neighboring nodes; the neighboring index is used to simultaneously represent the clustering coefficient and degree centrality of the node and all of the node's neighboring nodes; The neighborhood index of the node is integrated with the information entropy of the node to obtain the neighborhood entropy index of the node; The importance ranking of the nodes in the network topology graph is determined according to the neighborhood entropy index of the nodes.

2. The method according to claim 1, characterized in that The second parameter is calculated by the following formula: Among them, I c (i) is the second parameter of the i-th node, e -C(i) is the attenuation function of the clustering coefficient of the i-th node, C(i) is the clustering coefficient of the i-th node, Γ(i) is the set of adjacent nodes of the i-th node, i is a positive integer not greater than N, and N is the number of nodes in the network topology graph.

3. The method according to claim 2, characterized in that The neighborhood index of the node is fused with the information entropy of the node to obtain the neighborhood entropy index of the node, including: The neighboring index is normalized by the following formula to obtain the normalized neighboring index: Wherein, cn(i) is the neighborhood index of the i-th node, cn(j) is the neighborhood index of the j-th node, j is a positive integer from 1 to N, and CN(i) is the normalized neighborhood index of the i-th node; The neighborhood entropy index of the node is calculated by the following formula: Wherein, NACE(G) is the neighborhood entropy index of the node.

4. The method according to claim 1, wherein Determining the importance ranking of nodes in the network topology graph according to the neighborhood entropy index of the nodes includes: Determine the difference between the neighborhood entropy index of the node and the neighborhood entropy index of the first node as the neighborhood entropy centrality index of the target node, wherein the first node is all nodes in the network topology graph except the target node, and the target node is any node in the network topology graph; The values of the neighborhood entropy centrality indicators of the nodes are sorted according to a preset order to obtain the importance ranking of the nodes in the network topology graph.

5. The method according to claim 1, wherein After obtaining the network topology map, the method further includes: Obtaining a relationship strength index between the node and its adjacent nodes in the network topology graph; When the relationship strength index is greater than a preset threshold, the edge weight between the node and the adjacent node is determined to be 1; when the relationship strength index is not greater than the preset threshold, the edge weight between the node and the adjacent node is determined to be 0; An adjacency matrix for representing the network topology graph is constructed, wherein the rows of the adjacency matrix are used to represent each node in the network topology graph, and the columns of the adjacency matrix are used to represent the edge weights corresponding to the nodes.

6. The method according to claim 1, characterized in that The degree centrality of a node is determined based on the number of edges directly connected to the node.

7. The method according to claim 1, characterized in that The clustering coefficient of the node is determined according to the ratio of the actual number of edges between the node and its neighboring nodes to the maximum possible number of edges.

8. A data processing device, characterized in that: include: An acquisition module, configured to acquire a network topology map, wherein the network topology map includes a plurality of nodes; A first determination module is configured to determine a neighboring index of the node based on a first parameter and a second parameter, wherein the first parameter represents the sum of the degree centrality of the node and the degree centrality of all of the node's neighboring nodes; the second parameter represents the product of an attenuation function of the node's clustering coefficient and a target parameter, wherein the target parameter is the sum of the clustering coefficients of all of the node's neighboring nodes; and the neighboring index is configured to simultaneously represent the clustering coefficient and degree centrality of the node and all of its neighboring nodes. A fusion module, configured to fuse the neighborhood index of the node with the information entropy of the node to obtain the neighborhood entropy index of the node; The second determining module is used to determine the importance ranking of the nodes in the network topology diagram according to the neighborhood entropy index of the nodes.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the program executes the data processing method according to any one of claims 1 to 7 when running.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 7 is implemented.

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