Core object determination method and device, equipment and storage medium
By dividing the target objects into communities and calculating their importance and betweenness centrality, the core objects are determined, which solves the problem of insufficient accuracy of mining results in existing technologies and achieves higher mining accuracy.
Patent Information
- Application Number
- CN202511040879.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-28
- Publication Date
- 2025-10-10
AI Technical Summary
When mining core members, the existing technology has a relatively single analysis angle, resulting in insufficient accuracy of the mining results.
By determining multiple target objects, dividing them into target communities, and calculating the evaluation parameters of each target object based on importance and betweenness centrality, the core object is determined.
The accuracy of core member mining is improved, and the accuracy of mining results is enhanced by combining the importance and betweenness centrality perspectives for analysis.
Smart Images
Figure CN120765373A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to the field of data mining technology, and in particular to a core object determination method, apparatus, device, and storage medium. Background Art
[0002] Money laundering is closely linked to organized crime, such as drug trafficking and organized crime. Its methods have gradually evolved from scattered individual operations to organized gangs. This shift poses unprecedented challenges to financial institutions' anti-money laundering efforts. Existing technologies for identifying core members have a limited analytical perspective, and the accuracy of the results needs to be improved. Summary of the Invention
[0003] An embodiment of the present invention provides a core object determination method, apparatus, device and storage medium. The technical solution of the embodiment of the present invention solves the problem in the prior art that the core member mining angle is single, resulting in insufficient accuracy of the mining results. Core member mining can be performed based on the perspectives of importance and betweenness centrality, thereby improving the accuracy of the mining results.
[0004] In a first aspect, an embodiment of the present invention provides a method for determining a core object, the method comprising:
[0005] Determine multiple target objects, divide the multiple target objects into at least one target community; for each target community, determine a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; determine at least one core object in the target community based on the target evaluation parameters of the target objects in the target community.
[0006] In a second aspect, an embodiment of the present invention provides a core object determination device, the device comprising:
[0007] A target community determination module is used to determine multiple target objects and divide the multiple target objects into at least one target community; an evaluation parameter determination module is used to determine, for each target community, a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; a core object mining module is used to determine at least one core object in the target community based on the target evaluation parameters of the target objects in the target community.
[0008] In a third aspect, an embodiment of the present invention provides a computer device, the computer device comprising:
[0009] one or more processors;
[0010] a memory for storing one or more programs;
[0011] When the one or more programs are executed by the one or more processors, the one or more processors implement the core object determination method described in any embodiment.
[0012] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the core object determination method described in any embodiment.
[0013] The technical solution provided by the embodiment of the present invention determines multiple target objects and divides the multiple target objects into at least one target community; for each target community, determines the first evaluation parameter and the second evaluation parameter of each target object in the target community, and determines the target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; based on the target evaluation parameters of the target objects in the target community, at least one core object in the target community is determined. The technical solution of the embodiment of the present invention solves the problem of the single angle of core member mining in the existing technology, which leads to insufficient accuracy of mining results. It can perform core member mining based on the perspectives of importance and betweenness centrality, thereby improving the accuracy of mining results. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 This is a flow chart of a method for determining a core object provided by an embodiment of the present invention;
[0015] Figure 2 This is a flow chart of another method for determining a core object provided by an embodiment of the present invention;
[0016] Figure 3 This is a workflow diagram for determining a core object provided by an embodiment of the present invention;
[0017] Figure 4 is a structural diagram of a core object determination device provided by an embodiment of the present invention;
[0018] Figure 5 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The acquisition, storage, use, processing, etc. of data in the technical solutions of the embodiments of the present invention comply with the relevant provisions of national laws and regulations.
[0020] Figure 1 This is a flow chart of a core object determination method provided by an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios where core members are mined from multiple target objects. The method can be executed by a core object determination device, which can be implemented by software and / or hardware.
[0021] like Figure 1 As shown, the core object determination method includes the following steps:
[0022] S110: Determine multiple target objects, and divide the multiple target objects into at least one target community.
[0023] Among them, the target object may be a target object that needs to be analyzed for financial transactions. The technical solution of the embodiment of the present invention can determine the gang leader or core members by analyzing multiple target objects. Furthermore, the target community can be a collection of multiple target objects with associated relationships. Multiple target objects under the same target community can be understood as possibly participating in a gang crime together. Specifically, the crime association relationship between the target objects can be extracted from the transaction data between the multiple target objects, and then the multiple target objects can be divided based on the crime association relationship to obtain the target community.
[0024] S120 : For each target community, determine a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter.
[0025] Among them, the first evaluation parameter is used to represent the importance of the target object in the target community. The second evaluation parameter is used to represent the betweenness centrality of the target object in the target community. Betweenness centrality is an indicator used to measure the degree of node intermediary in a network, which aims to evaluate the node's control over the flow of information. Specifically, the first evaluation parameter and the second evaluation parameter of each target object can be determined respectively according to the preset importance calculation formula and the betweenness centrality calculation formula. The target evaluation parameter is used to represent the total influence of the target object in the target community. Specifically, for each target object, the first evaluation parameter and the second evaluation parameter of the target object can be weighted and summed to obtain the corresponding target evaluation parameter.
[0026] S130: Determine at least one core object in the target community according to target evaluation parameters of target objects in the target community.
[0027] The core object may be a target object with greater influence in the target community. Specifically, the target objects in the target community may be ranked (in descending order) based on the target evaluation parameter, and the target objects ranked at the top of the preset ranking may be used as the core objects of the target community.
[0028] The technical solution provided by the embodiment of the present invention determines multiple target objects and divides the multiple target objects into at least one target community; for each target community, determines the first evaluation parameter and the second evaluation parameter of each target object in the target community, and determines the target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; based on the target evaluation parameters of the target objects in the target community, at least one core object in the target community is determined. The technical solution of the embodiment of the present invention solves the problem of the single angle of core member mining in the existing technology, which leads to insufficient accuracy of mining results. It can perform core member mining based on the perspectives of importance and betweenness centrality, thereby improving the accuracy of mining results.
[0029] Figure 2This is another flow chart of a core object determination method provided by an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios where core members are mined from multiple target objects. Based on the above embodiment, this embodiment further explains how to divide multiple target objects into at least one target community; how to respectively determine the first evaluation parameter and the second evaluation parameter of each target object in the target community; and how to determine at least one core object in the target community based on the target evaluation parameters of the target objects in the target community. The device can be implemented by software and / or hardware and integrated into a computer device with application development capabilities.
[0030] like Figure 2 As shown, the core object determination method includes the following steps:
[0031] S210: Determine multiple target objects, obtain transaction data between the target objects, and establish a transaction relationship network based on the transaction data.
[0032] Among them, the target object may be a target object that needs to be analyzed for financial transactions. The technical solution of the embodiment of the present invention can determine the gang leader or core members by analyzing multiple target objects. Furthermore, the transaction data can be data on financial transactions between target objects. Specifically, the financial income and expenditure data corresponding to each target object can be obtained separately, and then a large amount of daily consumption transaction data can be eliminated from it, and finally the transaction data between the target objects can be extracted. The transaction relationship network can be used to represent the financial transaction relationship between target objects. Specifically, multiple target objects can be used as vertices in the transaction relationship network, and then the vertices of the target objects where transactions occur can be connected to construct a transaction relationship network.
[0033] S220: Divide the transaction relationship network into multiple sub-relationship networks, and use each sub-relationship network as a target community.
[0034] A sub-network can be a partial network within a transaction network. Specifically, the transaction network can be split based on transaction attribute data between target objects, resulting in multiple sub-networks. A target community can be a collection of multiple target objects. Each sub-network can serve as a target community. Multiple target objects within the same target community can be considered to be participating in a group-based criminal activity.
[0035] Optionally, the transaction relationship network is divided into multiple sub-networks, including: assigning an initial identity label to each unlabeled node in the sub-network, and in the subsequent iterative update process, for each unlabeled node, selecting the identity label that appears most frequently among its neighboring nodes to update its own identity label; when the iterative update end condition is met, the nodes with the same identity label are classified into the same sub-network.
[0036] The initial identity tag can be a user-defined tag representing the node's unique identity information. By updating the node's own identity tag with the most frequently appearing identity tag among its neighboring nodes, this can simulate local information diffusion within the network, encouraging closely connected groups of nodes to gradually form a consensus tag. Neighboring nodes can be directly adjacent to the node. The iterative update can terminate when a set percentage of node labels remain unchanged or when a preset number of iterations has been reached.
[0037] The nodes in the transaction relationship network can be divided into two parts, one is the labeled nodes (known identity labels), and the other is the unlabeled nodes (unknown identity labels that need to be predicted). During initialization, the labels of all labeled nodes are set to their real labels, and all unlabeled nodes are initialized with a unique, virtual initial identity label. In the iterative propagation stage, for each unlabeled node, the number of labels of all its direct neighbor nodes is counted, and the label with the most occurrences is used as the label of the unknown node. The end condition of the iterative update can be that the node labels no longer change for more than a set proportion or the preset number of iterations is reached. Finally, nodes with the same label are assigned to the same target community.
[0038] Optionally, if the number of target objects in a target community is less than a preset threshold (referred to as a small group), the target community can be merged into other target communities. Specifically, for each target object in a small group, all adjacent target objects of the target object are obtained, and the communities to which the adjacent target objects belong are counted. The target object is then moved to the large group with the largest number of appearances (i.e., the target community containing target objects with a number greater than the preset threshold); if the adjacent target objects of the target object are all in the small group, the target object is randomly moved to a large group.
[0039] S230 : For each target community, determine a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter according to the first evaluation parameter and the second evaluation parameter.
[0040] The first evaluation parameter is used to represent the importance of the target object in the target community. Optionally, determining the first evaluation parameter of the target object includes: determining other transaction objects in the target community that initiate transactions with the target object; for each other transaction object, combining the first evaluation parameter and the corresponding out-degree value of the other transaction object to obtain a reference evaluation parameter corresponding to the other transaction object; and determining the first evaluation parameter of the target object based on the reference evaluation parameters of all other transaction objects and a preset jump probability.
[0041] The out-degree value is the number of outgoing transactions with other transaction objects. The reference evaluation parameter can be an evaluation parameter of the importance of the target object from the perspective of other transaction objects. Specifically, the first evaluation parameter corresponding to each transaction object can be compared with its corresponding out-degree value, and the resulting ratio can be used as the reference evaluation parameter. Furthermore, the reference evaluation parameters of all other transaction objects can be summed, multiplied by a preset jump probability, and then added to the sum to obtain the first evaluation parameter of the target object.
[0042] Exemplarily, the calculation formula of the first evaluation parameter of the target object is as follows:
[0043]
[0044] Where PR(v) is the first evaluation parameter of the target object v; d is the preset jump probability; L(v) is the set of other transaction objects that initiate transactions with the target object v; and C(i) is the number of expenditure transactions of transaction object i.
[0045] Furthermore, the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community. Among them, betweenness centrality is an indicator used to measure the degree of node intermediary in the network, which aims to evaluate the node's control over the flow of information. Optionally, determining the second evaluation parameter of the target object includes: forming an other object pair with every two other objects other than the target object in the target community; for each other object pair, determining the number of shortest transaction paths between the two other objects in the other object pair, and determining the number of target transaction paths passing through the target object in the shortest transaction path; comparing the number of target transaction paths with the number of shortest transaction paths, and using the obtained ratio as the second evaluation sub-parameter of the other object pair; summing up the second evaluation sub-parameters of all other object pairs in the target community to obtain the second evaluation parameter of the target object.
[0046] The second evaluation sub-parameter may be an evaluation parameter of the betweenness centrality of a single other object to the target object. Specifically, the calculation formula of the second evaluation parameter of the target object is as follows:
[0047]
[0048] Among them, other objects s and other objects t constitute an other object pair, M(v) represents the set of other object pairs corresponding to the target object v, σ st represents the total number of shortest paths from other objects s to other objects t, σ st (v) represents the total number of target objects v in the shortest path from target object s to target object t.
[0049] The core concept of this calculation method is that the importance of a node is determined by how often it appears on the shortest paths between pairs of other nodes. The stronger a node's ability to bridge different regions is, the higher its betweenness centrality score. Betweenness centrality reveals the bridging role of a node in the global structure and complements node importance assessment to support in-depth analysis of complex networks.
[0050] Finally, the target evaluation parameter is used to represent the total influence of the target object in the target community. Specifically, for each target object, the first evaluation parameter and the second evaluation parameter of the target object can be weighted and summed to obtain the corresponding target evaluation parameter.
[0051] S240: Determine at least one core object in the target community according to the target evaluation parameters of the target objects in the target community.
[0052] The core object may be a target object with greater influence in the target community. Specifically, the target objects in the target community may be ranked (in descending order) based on the target evaluation parameter, and the target objects ranked at the top of the preset ranking may be used as the core objects of the target community.
[0053] For example, in order to better understand the technical solution provided by the present invention, a specific embodiment is introduced below: Figure 3 This is a workflow diagram for determining a core object provided by an embodiment of the present invention. Figure 3 As shown, the technical solution of the embodiment of the present invention is roughly divided into six steps. The first step is to use keywords to eliminate daily consumption transactions in the system to obtain non-daily consumption transaction data; the second step is to construct the above transaction information into a transaction network: the vertices in the transaction network are target objects, and the weights of the edges between the vertices are the cumulative transaction amounts of the target objects at both ends; the third step is to group the transaction network constructed in the second step; the fourth step is to further process the results after grouping. For groups with less than a threshold number of target objects in the group, each target object in the group is moved to a large group near the vertex; the fifth step is to use the importance algorithm and the betweenness centrality algorithm to calculate the weighted risk coefficient of each target object in the transaction network, that is, the risk coefficient score; the sixth step is to combine the calculation results of the fourth and fifth steps to warn the core objects with the top risk coefficients in each group.
[0054] The technical solution provided by the embodiments of the present invention determines multiple target objects, obtains transaction data between the target objects, and establishes a transaction relationship network based on the transaction data; divides the transaction relationship network into multiple sub-relationship networks, and regards each sub-relationship network as a target community; for each target community, determines first and second evaluation parameters for each target object in the target community, and determines target evaluation parameters based on the first and second evaluation parameters; and determines at least one core object in the target community based on the target evaluation parameters of the target objects in the target community. The technical solution of the embodiments of the present invention solves the problem of the existing technology of single-angle core member mining, which leads to insufficient mining result accuracy. It can mine core members based on the perspectives of importance and betweenness centrality, thereby improving the accuracy of mining results.
[0055] Figure 4 This is a structural diagram of a core object determination device provided by an embodiment of the present invention. The embodiment of the present invention can be applied to the scenario of mining core members from multiple target objects. The device can be implemented by software and / or hardware and integrated into a computer device with application development capabilities.
[0056] like Figure 4 As shown, the core object determination device includes: a target community determination module 310 , an evaluation parameter determination module 320 and a core object mining module 330 .
[0057] Among them, the target community determination module 310 is used to determine multiple target objects and divide the multiple target objects into at least one target community; the evaluation parameter determination module 320 is used to determine the first evaluation parameter and the second evaluation parameter of each target object in each target community, and determine the target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; the core object mining module 330 is used to determine at least one core object in the target community based on the target evaluation parameters of the target objects in the target community.
[0058] The technical solution provided by the embodiment of the present invention determines multiple target objects and divides the multiple target objects into at least one target community; for each target community, determines the first evaluation parameter and the second evaluation parameter of each target object in the target community, and determines the target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; based on the target evaluation parameters of the target objects in the target community, at least one core object in the target community is determined. The technical solution of the embodiment of the present invention solves the problem of the single angle of core member mining in the existing technology, which leads to insufficient accuracy of mining results. It can perform core member mining based on the perspectives of importance and betweenness centrality, thereby improving the accuracy of mining results.
[0059] In an optional embodiment, the target community determination module 310 is specifically used to: obtain transaction data between the target objects and establish a transaction relationship network based on the transaction data; wherein the vertices in the transaction relationship network are multiple target objects; divide the transaction relationship network into multiple sub-relationship networks, and each sub-relationship network is used as a target community.
[0060] In an optional embodiment, the target community determination module 310 includes: a relationship network division unit, which is used to: assign an initial identity label to each unlabeled node in the sub-relationship network, and in the subsequent iterative update process, for each unlabeled node, select the identity label that appears most frequently among its neighbor nodes to update its own identity label; when the iterative update end condition is met, the nodes with the same identity label are classified into the same sub-relationship network.
[0061] In an optional embodiment, the evaluation parameter determination module 320 includes: a first evaluation parameter determination unit, used to: determine other transaction objects in the target community that initiate transactions with the target object; for each other transaction object, obtain the reference evaluation parameter corresponding to the other transaction object by using the first evaluation parameter and the corresponding out-degree value corresponding to the other transaction object; determine the first evaluation parameter of the target object based on the reference evaluation parameters of all other transaction objects and the preset jump probability.
[0062] In an optional embodiment, the evaluation parameter determination module 320 includes: a second evaluation parameter determination unit, used to: form an other object pair with every two other objects other than the target object in the target community; for each other object pair, determine the number of shortest transaction paths between two other objects in the other object pair, and determine the number of target transaction paths passing through the target object in the shortest transaction path; compare the number of target transaction paths with the number of shortest transaction paths, and use the obtained ratio as the second evaluation sub-parameter of the other object pair; add the second evaluation sub-parameters of all other object pairs in the target community to obtain the second evaluation parameter of the target object.
[0063] In an optional implementation, the core object determination module 330 is specifically configured to: sort the target objects in the target community based on the target evaluation parameter, and select the target objects with the highest preset ranking in the ranking as the core objects of the target community.
[0064] In an optional embodiment, the relationship network division unit includes: a community merging subunit, configured to merge the target community into other target communities when the number of target objects in the target community is less than a preset number threshold.
[0065] The core object determination device provided in the embodiment of the present invention can execute the core object determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0066] Figure 5 A schematic structural diagram of a computer device provided in an embodiment of the present invention. Figure 5 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 5 The computer device 12 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention. The computer device 12 can be any terminal device with computing capabilities and can be configured in the core object determination device.
[0067] like Figure 5 As shown, computer device 12 is implemented as a general-purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 that connects various system components (including system memory 28 and processing unit 16).
[0068] The bus 18 may be one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
[0069] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.
[0070] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 5 Not shown, often called a "hard drive"). Although Figure 5 Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.
[0071] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.
[0072] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may occur through an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. Figure 5 As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0073] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the core object determination method provided in an embodiment of the present invention, which includes:
[0074] Determine multiple target objects, divide the multiple target objects into at least one target community; for each target community, determine a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; determine at least one core object in the target community based on the target evaluation parameters of the target objects in the target community.
[0075] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for determining a core object provided in any embodiment of the present invention is implemented, including:
[0076] Determine multiple target objects, divide the multiple target objects into at least one target community; for each target community, determine a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; determine at least one core object in the target community based on the target evaluation parameters of the target objects in the target community.
[0077] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.
[0078] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0079] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
[0080] Computer program code for performing the operations of the present invention can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as C, Java, Smalltalk, C++, C#, and Python, and also conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0081] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
[0082] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.
Claims
1. A method for determining a core object, characterized in that: include: Determine multiple target objects, and divide the multiple target objects into at least one target community; For each target community, determining a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determining a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; At least one core object in the target community is determined according to target evaluation parameters of target objects in the target community.
2. The method according to claim 1, characterized in that The step of dividing the multiple target objects into at least one target community includes: Acquire transaction data between the target objects, and establish a transaction relationship network based on the transaction data; wherein the vertices in the transaction relationship network are multiple target objects; The transaction relationship network is divided into multiple sub-networks, and each sub-network is used as a target community.
3. The method according to claim 2, characterized in that The transaction relationship network is divided into multiple sub-relationship networks, including: Assigning an initial identity tag to each unlabeled node in the sub-network, and in subsequent iterative updates, for each unlabeled node, selecting the identity tag that appears most frequently among its neighboring nodes to update its own identity tag; When the iterative update end conditions are met, nodes with the same identity labels are grouped into the same sub-network.
4. The method according to claim 1, wherein Determining a first evaluation parameter of the target object includes: Determine other transaction partners in the target community who initiate transactions with the target partner; For each other transaction object, obtain the reference evaluation parameter corresponding to the other transaction object by combining the first evaluation parameter and the corresponding out-degree value corresponding to the other transaction object; The first evaluation parameter of the target object is determined according to the reference evaluation parameters of all other transaction objects and the preset jump probability.
5. The method according to claim 1, wherein The determining of the second evaluation parameter of the target object includes: Every two other objects in the target community except the target object form an other object pair; For each other object pair, determine the number of shortest transaction paths between two other objects in the other object pair, and determine the number of target transaction paths in the shortest transaction paths that pass through the target object; Comparing the number of target transaction paths with the number of shortest transaction paths, and using the obtained ratio as a second evaluation sub-parameter for the other object pairs; The second evaluation sub-parameters of all other object pairs in the target community are summed to obtain the second evaluation parameter of the target object.
6. The method according to claim 1, characterized in that The determining, based on the target evaluation parameters of the target objects in the target community, at least one core object in the target community includes: The target objects in the target community are ranked based on the target evaluation parameters, and the target objects with the top preset rankings in the rankings are used as the core objects of the target community.
7. The method according to claim 2, characterized in that The method further comprises: When the number of target objects in the target community is less than a preset number threshold, the target community is merged into other target communities.
8. A core object determination device, characterized in that: The device comprises: A target community determination module is used to determine multiple target objects and divide the multiple target objects into at least one target community; An evaluation parameter determination module is configured to determine, for each target community, a first evaluation parameter and a second evaluation parameter for each target object in the target community, and determine a target evaluation parameter based on the first evaluation parameter and the second evaluation parameter; wherein the first evaluation parameter is used to represent the importance of the target object in the target community, and the second evaluation parameter is used to represent the betweenness centrality of the target object in the target community; The core object mining module is configured to determine at least one core object in the target community according to target evaluation parameters of the target objects in the target community.
9. A computer device, characterized in that: The computer device comprises: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the core object determination method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the core object determination method according to any one of claims 1 to 7 is implemented.