Method, device, computer equipment and medium for identifying the importance of network nodes

Abstract

The present disclosure provides a method, apparatus, computer device, and medium for identifying the importance of network nodes, including: obtaining a node network structure; determining betweenness centrality percentage data for each network node; determining target influence representation data for each network node relative to the target node based on the betweenness centrality percentage data for each network node and the node degree value of the target node; and determining an importance identification value for the target node based on the target influence representation data for each network node relative to the target node. Thus, by narrowing the search range of the target node's influencing nodes, a node importance influence representation is established, thereby comprehensively identifying the importance of the target node, facilitating more accurate system operational analysis of the network nodes.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method, apparatus, computer device, and medium for identifying the importance of a network node. Background Art

[0002] The study of node importance in complex networks has become a hot topic, driven by the rapid development of diverse fields, including social networks, communications technology, bioinformatics, and socioeconomics. Its application is extensive. For example, in the communications field, nodes may represent network devices such as switches and routers. Determining node importance helps optimize network structure, improve fault tolerance and anti-attack capabilities, and ensure efficient and stable network operation.

[0003] In the transportation sector, nodes can represent transportation hubs, such as airports and train stations, while edges represent transportation routes. Understanding the importance of these nodes facilitates transportation planning and optimization, improving the efficiency and robustness of the entire transportation network. In the military, information related to various weapons and equipment, combat units, various combat elements, and their relationships in modern warfare can be abstracted into a complex network topology consisting of nodes and their connecting edges. By analyzing this abstracted complex network, high-value targets or key nodes can be more accurately and effectively identified, providing precise targeting support to decision-makers. Therefore, research on networked target importance calculation methods is of great significance for applications in the communications, transportation, and military fields. Summary of the Invention

[0004] The embodiments described herein provide a method, apparatus, computer device, and medium for identifying the importance of a network node, which overcome the above-mentioned problems.

[0005] In a first aspect, according to the present disclosure, a method for identifying the importance of a network node is provided, comprising:

[0006] Acquire a node network structure, wherein the node network structure includes a plurality of network nodes, and different network nodes have a mutual influence relationship;

[0007] Determining betweenness centrality ratio data for each of the network nodes, the betweenness centrality ratio data being used to describe the ratio of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index values ​​of all network nodes, wherein the betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes;

[0008] Based on the betweenness centrality ratio data of each of the network nodes and the node degree value of the target node, determining the target influence degree characterization data of each of the network nodes relative to the target node, the target influence degree characterization data including the influence degree characterization data of each preset node relative to the target node, the preset node being a network node in the node network structure that has a transit node with the target node, or a network node that is an adjacent node to the target node in the node network structure, and the target node is one of the multiple network nodes;

[0009] An importance identification value of the target node is determined based on the target influence characterization data of each network node relative to the target node.

[0010] In a second aspect, according to the present disclosure, a device for identifying the importance of a network node is provided, comprising:

[0011] An acquisition module is used to acquire a node network structure, wherein the node network structure includes a plurality of network nodes, and different network nodes have a mutual influence relationship;

[0012] a first determining module, configured to determine betweenness centrality ratio data of each of the network nodes, wherein the betweenness centrality ratio data is used to describe the ratio of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index values ​​of all network nodes, and the betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes;

[0013] A second determination module is configured to determine target influence characterization data of each network node relative to the target node based on the betweenness centrality proportion data of each network node and the node degree value of the target node, wherein the target influence characterization data includes influence characterization data of each preset node relative to the target node, the preset node being a network node in the node network structure that has a transit node with the target node, or a network node that is an adjacent node to the target node in the node network structure, and the target node is one of the plurality of network nodes;

[0014] The third determination module is configured to determine an importance identification value of the target node based on the target influence characterization data of each network node relative to the target node.

[0015] In a third aspect, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for identifying the importance of a network node in any of the above embodiments are implemented.

[0016] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for identifying the importance of a network node in any of the above embodiments are implemented.

[0017] The embodiment of the present application provides a method for identifying the importance of network nodes, which obtains a node network structure, wherein the node network structure includes multiple network nodes, and different network nodes have a mutual influence relationship; determines the betweenness centrality ratio data of each network node, and the betweenness centrality ratio data is used to describe the ratio characterization data of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index value of all network nodes, and the betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes; based on the betweenness centrality ratio data of each network node and the node degree value of the target node, determines the target influence degree characterization data of each network node relative to the target node, and the target influence degree characterization data includes the influence degree characterization data of each preset node relative to the target node, and the preset node is a network node in the node network structure that has a transit node between it and the target node, or a network node that is an adjacent node to the target node in the node network structure, and the target node is one of multiple network nodes; based on the target influence degree characterization data of each network node relative to the target node, determines the importance identification value of the target node. In this way, by narrowing the search range of the target node's influencing nodes, a node importance influence representation is established, and then the importance of the target node is comprehensively identified, which facilitates more accurate system combat analysis of network nodes.

[0018] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to more clearly understand the technical means of the embodiments of the present application, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be noted that the drawings described below only relate to some embodiments of the present disclosure and are not intended to limit the present disclosure.

[0020] Figure 1 This is a flow chart of a method for identifying the importance of a network node provided by the present disclosure.

[0021] Figure 2 It is a structural diagram of a device for identifying the importance of a network node provided by the present disclosure.

[0022] Figure 3It is a structural diagram of a computer device provided by the present disclosure.

[0023] It should be noted that the elements in the drawings are schematic and not drawn to scale. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work also fall within the scope of protection of the present disclosure.

[0025] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the specification and the relevant art, and will not be interpreted in an idealized or overly formal manner unless otherwise explicitly defined herein. As used herein, a statement that two or more parts are "connected" or "coupled" together shall mean that the parts are joined together either directly or through one or more intermediate components.

[0026] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase "embodiment" in various places in the specification does not necessarily refer to the same embodiment, nor does it necessarily refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0027] The term "and / or" in this document simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists, A and B exist simultaneously, and B exists. Additionally, the character " / " in this document generally indicates that the related objects are in an "or" relationship. Terms such as "first" and "second" are used solely to distinguish one component (or portion of a component) from another component (or portion of a component).

[0028] In the description of this application, unless otherwise specified, "plurality" means more than two (including two), and similarly, "multiple groups" means more than two (including two).

[0029] This embodiment takes into account the node's ability to control the information propagation flow in the network topology structure and the influence of neighboring nodes within two hops (secondary jumps) on the importance of the target node, thereby constructing a node importance influence matrix. By integrating global path and local topology information, the importance of the target node is more comprehensively identified, thereby enabling a more accurate system analysis of network nodes.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0031] Figure 1 FIG. 1 is a flow chart of a method for identifying the importance of a network node provided by an embodiment of the present disclosure. Figure 1 As shown in FIG, the specific process of the network node importance identification method includes:

[0032] S110: Acquire a node network structure, where the node network structure includes multiple network nodes.

[0033] Network nodes can be used to describe the networked representation of combat targets. Accordingly, the node network structure is a network topology consisting of multiple combat targets. Different network nodes influence each other, meaning that different combat targets have different attributes that influence the combat process.

[0034] S120: Determine the betweenness centrality ratio data of each network node.

[0035] Among them, the betweenness centrality ratio data is used to describe the ratio of the betweenness centrality index value of the corresponding network node to the betweenness centrality index value of all network nodes. The betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes.

[0036] In some embodiments, determining betweenness centrality percentage data for each network node includes:

[0037] Get the betweenness centrality index value of each network node included in the node network structure.

[0038] Among them, the importance of network nodes is related to their control power in the node network structure. The stronger the control power over information flow, the higher the importance.

[0039] The betweenness centrality index value of a network node can be used as a measure of the centrality of the node network structure through the shortest path.

[0040] The betweenness centrality index value of each network node can be determined based on data representing the number of all network nodes included in the node network structure and the number of corresponding network nodes included in the shortest path between every two network nodes.

[0041] In some embodiments, obtaining a betweenness centrality index value of each network node included in the node network structure includes:

[0042] Obtain the number of nodes of all network nodes included in the node network structure; determine the number representation data of the corresponding network nodes included in the node shortest path between every two network nodes; based on the number of nodes of all network nodes included in the node network structure and the number representation data of the corresponding network nodes included in the node shortest path between every two network nodes, determine the betweenness centrality index value of each network node included in the node network structure.

[0043] The corresponding network node is a network node for which the betweenness centrality index value needs to be determined, and every two network nodes are any two reference network nodes corresponding to the corresponding network node.

[0044] For example, the following formula (1) can be used to determine the betweenness centrality index value of each network node included in the node network structure based on the number of nodes of all network nodes included in the node network structure and the number of target nodes included in the shortest path between every two network nodes.

[0045]

[0046] In formula (1), LD i Represents network node v i The betweenness centrality index value; N represents the number of nodes in the network; σ st Represents network node v s and network node v t The number of shortest paths between Indicates that these paths contain network nodes v i The number of

[0047] Based on the betweenness centrality index value of each network node, the sum representation value of the betweenness centrality index values ​​of all network nodes is determined.

[0048] Among them, the sum of the betweenness centrality index values ​​of all network nodes represents the value, which is the sum of the betweenness centrality index values ​​of each network node.

[0049] Based on the betweenness centrality index value of each network node and the sum of the betweenness centrality index values ​​of all network nodes, the betweenness centrality ratio data of each network node is determined.

[0050] In combination with the above example, the following formula (2) can be used to determine the betweenness centrality ratio data of each network node based on the betweenness centrality index value of each network node and the sum of the betweenness centrality index values ​​of all network nodes.

[0051]

[0052] In formula (2), L i Represents network node v i The proportion of betweenness centrality of It represents the sum of the betweenness centrality index values ​​of all network nodes.

[0053] Thus, by representing the data by the number of all network nodes included in the node network structure and the number of corresponding network nodes included in the shortest path between every two network nodes, the betweenness centrality index value of each network node included in the node network structure is effectively determined.

[0054] S130 : Determine target influence representation data of each network node relative to the target node based on the betweenness centrality ratio data of each network node and the node degree value of the target node.

[0055] The target influence data includes the influence of each preset node on the target node. The betweenness centrality ratio of network nodes can effectively reflect the significant influence of different neighboring nodes on the target node.

[0056] The preset node is a network node in a node network structure that has only one transit node between it and the target node, or a network node that is a neighboring node to the target node in the node network structure, i.e., there is no transit node between the preset node and the target node in the node network structure. The target node is one of multiple network nodes, i.e., the target node is any network node in the node network structure.

[0057] It should be noted that the transit node is a network node in the node network structure.

[0058] Because different neighboring nodes have different degrees of influence on the target node and their influence varies depending on their range, this embodiment expands the scope of neighbor node investigation from the nearest neighbor to neighbor nodes within a two-hop range (i.e., the description range of the preset node referred to above).

[0059] In some embodiments, determining target influence representation data of each network node relative to a target node based on betweenness centrality ratio data of each network node includes:

[0060] Based on the betweenness centrality ratio data of each network node, the initial influence degree characterization data of each network node relative to the target node is determined.

[0061] The initial influence representation data may include the influence representation data of each network node relative to the target node in the node network structure. The initial influence representation data may be determined by the discriminant influence parameter corresponding to each network node and the betweenness centrality ratio data of each network node.

[0062] In some embodiments, determining the initial influence representation data of each network node relative to the target node based on the betweenness centrality ratio data of each network node includes:

[0063] Obtain the discriminant influence parameters corresponding to each network node. The discriminant influence parameters corresponding to the network node are used to describe the discriminant influence relationship of the network node relative to the target node. Based on the discriminant influence parameters corresponding to each network node and the betweenness centrality ratio data of each network node, determine the initial influence degree characterization data of each network node relative to the target node.

[0064] Combined with the above example, formula (3) can be used to determine the initial influence degree representation data of each network node relative to the target node based on the discriminant influence parameter corresponding to each network node and the betweenness centrality ratio data of each network node.

[0065]

[0066] In formula (3), ω ij Represents the discrimination parameter of neighbor nodes within the two-hop range, that is, network node v j Relative to the target node v i The discriminant influence parameter of i The set of neighbor nodes within a two-hop range; the matrix elements on the diagonal position refer to the degree of influence of the network node on its own importance, which is 1.

[0067] The node importance influence matrix Lc constructed using the node betweenness centrality index can reflect the degree of influence of different neighboring nodes on the target node within the second-order range.

[0068] In some embodiments, if a network node satisfies the node adjacency condition with the target node in the node network structure, the discrimination influence parameter corresponding to the network node is a first preset value; if a network node does not satisfy the node adjacency condition with the target node in the node network structure, the discrimination influence parameter corresponding to the network node is a second preset value.

[0069] The node adjacent condition includes: a network node and a target node are adjacent nodes to each other in the node network structure, or there is only one transit node between a network node and the target node in the node network structure.

[0070] For example, if node v j For node v i Neighbor nodes within the two-hop range (i.e. node v j In the node network structure with node v i are adjacent nodes, or node v j In the node network structure with node v i There is only one transfer node between them), then ω ij =1; if node v j Not for node v i Neighbor nodes within the two-hop range (i.e. node v j In the node network structure with node v i There are at least two transfer nodes between them), then ω ij =0.

[0071] Obtaining the node degree value of the target node; optimizing the initial influence degree characterization data based on the node degree value of the target node to improve the initial influence degree characterization data and obtain the target influence degree characterization data.

[0072] The node degree of a target node describes the number of network nodes whose distance to the target node is less than a preset distance. A node's node degree reflects the number of its nearest neighbors and, to a certain extent, the importance of these neighbors to the target node.

[0073] By adding the node degree value of the target node to the initial influence degree representation data, the neighbor node influence matrix Hc within the two-hop range is improved.

[0074] In combination with the above example, formula (4) can be used to optimize the initial influence degree representation data based on the node degree value of the target node to obtain the target influence degree representation data.

[0075]

[0076] In formula (4), Represents node v j For node v i The greater the value of the node's betweenness centrality index and the degree value, the greater the importance of the node to the neighboring nodes within the two-hop range.

[0077] Therefore, by adding the node degree value of the target node on the basis of the initial influence degree representation data, the initial influence degree representation data is improved to obtain the importance influence representation of the neighboring nodes within the two-hop range of the target node.

[0078] S140 : Determine an importance identification value of the target node based on the target influence characterization data of each network node relative to the target node.

[0079] Among them, the importance of the target node is affected by both its neighboring nodes and itself. Based on this, in order to obtain a more comprehensive target node importance identification value, this embodiment combines the target influence degree representation data of the target node and the betweenness centrality of the target node itself to achieve effective identification of the target node's importance identification value.

[0080] In some embodiments, determining the importance identification value of the target node based on the target influence characterization data of each network node relative to the target node includes:

[0081] Get the betweenness centrality index value of the target node.

[0082] As shown in formula (1), the betweenness centrality index value BC of the target node is i It can be calculated using the following formula (5).

[0083]

[0084] Based on the betweenness centrality index value of the target node and the target influence degree characterization data of each network node relative to the target node, the importance identification value of the target node is determined.

[0085] The target influence degree characterization data of the target node can be determined based on the discriminant influence parameters of the network nodes, the node degree value of the target node, and the betweenness centrality ratio data of each network node.

[0086] In combination with the above example, formula (6) can be used to determine the importance identification value of the target node based on the betweenness centrality index value of the target node and the target influence degree representation data of each network node relative to the target node.

[0087]

[0088] In formula (6), BDC i Represents the target node v i Importance identification value; k i Represents the target node v i The node degree value of Represents node v j Relative to the target node vi The target impact degree representation data.

[0089] This embodiment considers the node's ability to control the information propagation flow in the network topology and the influence of neighboring nodes within a two-hop range on the importance of the target node, thereby constructing a node importance influence matrix. By integrating global path and local topology information, the importance of the target node is more comprehensively identified, thereby enabling more accurate attacks on the target node in the network.

[0090] Among them, the node betweenness centrality is preliminarily obtained. The importance of a node is related to its control power in the network. The stronger the control power over the information flow, the higher the importance.

[0091] The preliminary construction of the neighbor node influence matrix within the two-hop range,When identifying the importance of the target node, it is necessary to consider the influence of the neighbor node attributes, and construct the node importance influence matrix based on this idea.

[0092] The improvement of the neighbor node influence matrix within the two-hop range and the node importance influence matrix constructed using the node betweenness centrality index reflect the influence degree of different neighbor nodes on the target node within the second-order range.

[0093] In the calculation of node importance identification value, the importance of the target node is affected by both neighboring nodes and itself. Therefore, in order to obtain a more comprehensive target node importance identification value, the betweenness centrality of the target node itself is considered when determining the final influence of the target node.

[0094] In this embodiment, by obtaining a node network structure, the node network structure includes multiple network nodes, and different network nodes have a mutual influence relationship; the betweenness centrality ratio data of each network node is determined, and the betweenness centrality ratio data is used to describe the ratio representation data of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index value of all network nodes, and the betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes; based on the betweenness centrality ratio data of each network node and the node degree value of the target node, the target influence degree representation data of each network node relative to the target node is determined, and the target influence degree representation data includes the influence degree representation data of each preset node relative to the target node, and the preset node is a network node in the node network structure that has a transit node with the target node, or a network node that is an adjacent node to the target node in the node network structure, and the target node is one of multiple network nodes; based on the target influence degree representation data of each network node relative to the target node, the importance identification value of the target node is determined. In this way, by narrowing the search range of the target node's influencing nodes, a node importance influence representation is established, and then the importance of the target node is comprehensively identified, which facilitates more accurate system combat analysis of network nodes.

[0095] Figure 2 This is a structural diagram of a device for identifying the importance of a network node provided in this embodiment, wherein the device for identifying the importance of a network node may include: an acquisition module 210, a first determination module 220, a second determination module 230 and a third determination module 240.

[0096] The acquisition module 210 is used to acquire a node network structure. The node network structure includes multiple network nodes, and different network nodes have a mutual influence relationship.

[0097] The first determination module 220 is used to determine the betweenness centrality ratio data of each network node. The betweenness centrality ratio data is used to describe the ratio characterization data of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index value of all network nodes. The betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes.

[0098] The second determination module 230 is used to determine the target influence degree characterization data of each network node relative to the target node based on the betweenness centrality ratio data of each network node and the node degree value of the target node, wherein the target influence degree characterization data includes the influence degree characterization data of each preset node relative to the target node, and the preset node is a network node in the node network structure that has a transit node with the target node, or a network node in the node network structure that is an adjacent node to the target node, and the target node is one of multiple network nodes.

[0099] The third determining module 240 is configured to determine an importance identification value of the target node based on the target influence characterization data of each network node relative to the target node.

[0100] In this embodiment, optionally, the first determining module 220 includes: a first acquiring unit, a first determining unit, and a second determining unit.

[0101] The first acquisition unit is used to obtain the betweenness centrality index value of each network node included in the node network structure.

[0102] The first determining unit is configured to determine a sum representation value of the betweenness centrality index values ​​of all network nodes based on the betweenness centrality index value of each network node.

[0103] The second determining unit is used to determine the betweenness centrality ratio data of each network node based on the betweenness centrality index value of each network node and the sum representation value of the betweenness centrality index values ​​of all network nodes.

[0104] In this embodiment, optionally, the first acquiring unit is specifically configured to:

[0105] Obtain the number of nodes of all network nodes included in the node network structure; determine the number representation data of the corresponding network nodes included in the node shortest path between every two network nodes; based on the number of nodes of all network nodes included in the node network structure and the number representation data of the corresponding network nodes included in the node shortest path between every two network nodes, determine the betweenness centrality index value of each network node included in the node network structure.

[0106] In this embodiment, optionally, the second determining module 230 includes: a third determining unit, a second acquiring unit, and a processing unit.

[0107] The third determining unit is used to determine the initial influence degree representation data of each network node relative to the target node based on the betweenness centrality ratio data of each network node.

[0108] The second acquiring unit is configured to acquire a node degree value of the target node, where the node degree value of the target node is used to describe the number of network nodes whose node distance to the target node is less than a preset distance.

[0109] The processing unit is used to optimize the initial influence degree representation data based on the node degree value of the target node, so as to improve the initial influence degree representation data and obtain the target influence degree representation data.

[0110] In this embodiment, optionally, the third determining unit is specifically configured to:

[0111] Obtain the discriminant influence parameters corresponding to each network node. The discriminant influence parameters corresponding to the network node are used to describe the discriminant influence relationship of the network node relative to the target node. Based on the discriminant influence parameters corresponding to each network node and the betweenness centrality ratio data of each network node, determine the initial influence degree characterization data of each network node relative to the target node.

[0112] In this embodiment, optionally, the third determining module 240 is specifically configured to:

[0113] Obtaining the betweenness centrality index value of the target node; determining the importance identification value of the target node based on the betweenness centrality index value of the target node and the target influence degree representation data of each network node relative to the target node.

[0114] In this embodiment, optionally, if a network node satisfies a node adjacency condition with a target node in a node network structure, the discrimination influence parameter corresponding to the network node is a first preset value; if a network node does not satisfy a node adjacency condition with a target node in a node network structure, the discrimination influence parameter corresponding to the network node is a second preset value; wherein, the node adjacency condition includes: a network node and a target node are adjacent nodes to each other in a node network structure, or, there is only one transit node between a network node and a target node in a node network structure.

[0115] The network node importance identification device provided by the present disclosure can execute the above method embodiments. Its specific implementation principles and technical effects can be found in the above method embodiments, and the present disclosure will not repeat them here.

[0116] The present application also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0117] The computer device includes a memory 310 and a processor 320 that are interconnected and communicate with each other via a system bus. It should be noted that the figure only shows a computer device with a memory 310 and a processor 320, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0118] Computer devices can be desktop computers, laptops, PDAs, cloud servers, etc. Computer devices can interact with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0119] The memory 310 includes at least one type of readable storage medium, including non-volatile memory or volatile memory, such as flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disk, optical disk, etc. The RAM may include static RAM or dynamic RAM. In some embodiments, the memory 310 may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 310 may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, or a Flash Card equipped on the computer device. Of course, the memory 310 may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory 310 is generally used to store the operating system and various application software installed on the computer device, such as the program code of the above-mentioned method. In addition, the memory 310 may also be used to temporarily store various types of data that have been output or are about to be output.

[0120] The processor 320 is generally used to perform the overall operation of the computer device. In this embodiment, the memory 310 is used to store program code or instructions, which include computer operating instructions. The processor 320 is used to execute the program code or instructions stored in the memory 310 or process data, such as the program code for running the above method.

[0121] In this article, a bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. This bus system can be divided into address buses, data buses, and control buses. For ease of illustration, the figure uses only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0122] Another embodiment of the present application further provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads the computer-readable program code stored in the computer-readable medium, enabling the processor to execute the functional actions specified in each step or combination of steps in the above method, and to generate a device that implements the functional actions specified in each block or combination of blocks in the block diagram.

[0123] Computer-readable media include but are not limited to electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any appropriate combination of the foregoing, the memory is used to store program codes or instructions, the program codes include computer operating instructions, and the processor is used to execute the program codes or instructions of the above-mentioned methods stored in the memory.

[0124] For the definitions of memory and processor, please refer to the description of the aforementioned computer device embodiment and will not be repeated here.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only 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 devices or units, which can be electrical, mechanical or other forms.

[0126] Each functional unit or module in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0127] 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 prior art 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, server, or network device, etc.) or a processor (processor) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0128] In the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" described in the present application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented with the aid of hardware comprising several different elements and with the aid of a suitably programmed computer. In a unit claim that lists several means, several units of these means may be embodied by the same hardware item. The use of first, second, and third etc. does not indicate any order and these words may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

[0129] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying the importance of network nodes, characterized in that: include: Acquire a node network structure, wherein the node network structure includes a plurality of network nodes, and different network nodes have a mutual influence relationship; Determining betweenness centrality ratio data for each of the network nodes, the betweenness centrality ratio data being used to describe the ratio of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index values ​​of all network nodes, wherein the betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between every two network nodes; Based on the betweenness centrality ratio data of each of the network nodes and the node degree value of the target node, determining the target influence degree characterization data of each of the network nodes relative to the target node, the target influence degree characterization data including the influence degree characterization data of each preset node relative to the target node, the preset node being a network node in the node network structure that has a transit node with the target node, or a network node that is an adjacent node to the target node in the node network structure, and the target node is one of the multiple network nodes; An importance identification value of the target node is determined based on the target influence characterization data of each network node relative to the target node.

2. The method according to claim 1, characterized in that Determining the betweenness centrality ratio data of each of the network nodes includes: Obtaining a betweenness centrality index value of each of the network nodes included in the node network structure; Based on the betweenness centrality index value of each of the network nodes, determining a sum representation value of the betweenness centrality index values ​​of all network nodes; Based on the betweenness centrality index value of each network node and the sum representation value of the betweenness centrality index values ​​of all network nodes, betweenness centrality proportion data of each network node is determined.

3. The method according to claim 2, characterized in that The obtaining of a betweenness centrality index value of each of the network nodes included in the node network structure includes: Obtaining the number of nodes of all network nodes included in the node network structure; Determine data representing the number of corresponding network nodes included in the shortest node path between each two of the network nodes; Based on the node number of all network nodes included in the node network structure and the data representing the number of corresponding network nodes included in the node shortest path between every two network nodes, the betweenness centrality index value of each network node included in the node network structure is determined.

4. The method according to claim 1, wherein The determining, based on the betweenness centrality ratio data of each network node and the node degree value of the target node, target influence representation data of each network node relative to the target node includes: Determining initial influence representation data of each network node relative to the target node based on the betweenness centrality ratio data of each network node; Obtaining a node degree value of the target node, where the node degree value of the target node is used to describe the number of neighboring network nodes whose node distance to the network node is less than a preset distance; Based on the node degree value of the target node, the initial influence degree representation data is optimized to improve the initial influence degree representation data and obtain the target influence degree representation data.

5. The method according to claim 4, characterized in that The determining, based on the betweenness centrality ratio data of each of the network nodes, the initial influence degree representation data of each of the network nodes relative to the target node includes: Obtaining a discrimination influence parameter corresponding to each of the network nodes, where the discrimination influence parameter corresponding to the network node is used to describe a discrimination influence relationship of the network node relative to the target node; Based on the discriminant influence parameter corresponding to each of the network nodes and the betweenness centrality ratio data of each of the network nodes, the initial influence degree representation data of each network node relative to the target node is determined.

6. The method according to claim 5, characterized in that The determining, based on the target influence characterization data of each network node relative to the target node, the importance identification value of the target node includes: Obtaining a betweenness centrality index value of the target node; An importance identification value of the target node is determined based on the betweenness centrality index value of the target node and target influence characterization data of each network node relative to the target node.

7. The method according to claim 5, characterized in that If a network node satisfies a node adjacent condition with the target node in the node network structure, the discrimination influence parameter corresponding to the network node is a first preset value; If a network node does not satisfy the node adjacency condition with the target node in the node network structure, the discrimination influence parameter corresponding to the network node is a second preset value; The node adjacent condition includes: the one network node and the target node are adjacent nodes to each other in the node network structure, or there is only one transit node between the one network node and the target node in the node network structure.

8. A device for identifying the importance of a network node, characterized in that: include: An acquisition module is used to acquire a node network structure, wherein the node network structure includes a plurality of network nodes, and different network nodes have a mutual influence relationship; The first determination module is used to determine the betweenness centrality ratio data of each network node, wherein the betweenness centrality ratio data is used to describe the ratio of the betweenness centrality index value of the corresponding network node relative to the betweenness centrality index value of all network nodes, and the betweenness centrality index value is determined based on the number of corresponding network nodes included in the shortest path between each two network nodes. A second determination module is configured to determine target influence characterization data of each network node relative to the target node based on the betweenness centrality proportion data of each network node and the node degree value of the target node, wherein the target influence characterization data includes influence characterization data of each preset node relative to the target node, the preset node being a network node in the node network structure that has a transit node with the target node, or a network node that is an adjacent node to the target node in the node network structure, and the target node is one of the plurality of network nodes; The third determination module is configured to determine an importance identification value of the target node based on the target influence characterization data of each network node relative to the target node.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method for identifying the importance of a network node as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying the importance of a network node as claimed in any one of claims 1 to 7 is implemented.

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