CN110222241B - Community segmentation method and device - Google Patents
Community segmentation method and device Download PDFInfo
- Publication number
- CN110222241B CN110222241B CN201910499900.8A CN201910499900A CN110222241B CN 110222241 B CN110222241 B CN 110222241B CN 201910499900 A CN201910499900 A CN 201910499900A CN 110222241 B CN110222241 B CN 110222241B
- Authority
- CN
- China
- Prior art keywords
- community
- application
- nodes
- determining
- attribute
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Abstract
The invention provides a community segmentation method and a community segmentation device, which relate to the technical field of data processing and comprise the following steps: acquiring application information of a target service in a network community to be segmented; constructing a bipartite graph of a network community to be segmented based on the application information; dividing a network community to be divided into a plurality of communities to obtain community division results; determining the number of application nodes contained in each community based on the bipartite graph; determining target communities with application nodes more than a first preset number in a plurality of communities; deleting target attribute nodes with connectivity degrees larger than a preset value in the target community; and determining the target community as the network community to be segmented, and returning to execute the step of segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm until the plurality of communities meet preset conditions, so that the technical problem that nodes in the network community are difficult to analyze due to the fact that the structure of the conventional network community is complex is solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a community segmentation method and device.
Background
As a common attribute of a network, a community structure is a partition of network nodes, where nodes in the same community are closely related, and nodes belonging to different communities are relatively loosely related. The method has important theoretical significance for analyzing the complex network topology, understanding the function, discovering the hidden mode and predicting the behavior of the complex network topology, and has wide application in biological networks, social networks and world wide webs.
However, because of the complex connection relationship of each node in the network community, when analyzing the nodes in the network community, the complex network community needs to be divided into a plurality of simpler network communities, thereby reducing the analysis difficulty. The existing network community segmentation method is low in segmentation efficiency, so that difficulty is caused in analysis of nodes in the network community.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for community segmentation, so as to alleviate the technical problem that the segmentation efficiency of the existing community segmentation method for network communities to be segmented is low.
In a first aspect, an embodiment of the present invention provides a community segmentation method, including: acquiring application information of a target service in a network community to be segmented; the application information includes at least one of: the application number, the identity information of the applicant, the living position information of the applicant and the communication information of the applicant; constructing a bipartite graph of the network community to be segmented based on the application information; the bipartite graph comprises: the system comprises an application node and/or an attribute node, wherein the application node represents an application number, and the attribute node represents at least one of identity information, living position information and communication information; dividing the network community to be divided into a plurality of communities to obtain community division results; determining the number of application nodes contained in each community based on the bipartite graph; determining target communities with application nodes more than a first preset number in a plurality of communities; deleting the target attribute nodes with the connectivity degree larger than a preset value in the target community; and determining the target community as a network community to be segmented, returning to execute the step of segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm until the plurality of communities meet a preset condition, and taking a community segmentation result meeting the preset condition as a target community segmentation result, wherein the preset condition is that the number of application nodes contained in each community is less than or equal to a second preset number.
Further, constructing the bipartite graph of the network community to be partitioned based on the application information includes: determining a set of application nodes and a set of attribute nodes according to the application information; and determining the incidence relation between each application node in the application node set and the attribute nodes in the attribute node set, and establishing the incidence relation between the application nodes in the application node set and the attribute nodes in the attribute node set according to the incidence relation to obtain the bipartite graph.
Further, determining the set of application nodes and the set of attribute nodes according to the application information includes: determining an application node in the network community to be segmented according to an application number in the application information; determining attribute nodes corresponding to each application node according to the identity information, the residence information and the communication information in the application information; determining all application nodes in the network community to be segmented as a set of the application nodes; and determining attribute nodes corresponding to all application nodes in the network community to be segmented as the set of the attribute nodes.
Further, the connectivity of the attribute nodes corresponding to the application nodes contained in each target community is calculated.
Further, the method further comprises: after the network community to be segmented is segmented into a plurality of communities by using a community discovery algorithm to obtain community segmentation results, the community to which each application node belongs is determined by using the community discovery algorithm.
In a first aspect, an embodiment of the present invention provides a community segmentation apparatus, including: an obtaining unit, a building unit, a dividing unit, a first determining unit and an executing unit, wherein,
the acquisition unit is used for acquiring application information of a target service in a network community to be segmented; the application information includes at least one of: the application number, the identity information of the applicant, the living position information of the applicant and the communication information of the applicant; the construction unit is used for constructing a bipartite graph of the network community to be segmented based on the application information; the bipartite graph comprises: the system comprises an application node and/or an attribute node, wherein the application node represents an application number, and the attribute node represents at least one of identity information, living position information and communication information; the segmentation unit is used for segmenting the network community to be segmented into a plurality of communities to obtain community segmentation results; determining the number of application nodes contained in each community based on the bipartite graph; the first determining unit is used for determining target communities with application nodes more than a first preset number in a plurality of communities; the execution unit is used for deleting the target attribute nodes with the connectivity degree larger than a preset value in the target community; and determining the target community as a network community to be segmented, returning to execute the step of segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm until the plurality of communities meet a preset condition, and taking a community segmentation result meeting the preset condition as a target community segmentation result, wherein the preset condition is that the number of application nodes contained in each community is less than or equal to a second preset number.
Further, the construction unit is further configured to: determining a set of application nodes and a set of attribute nodes according to the application information; and determining the incidence relation between each application node in the application node set and the attribute nodes in the attribute node set, and establishing the incidence relation between the application nodes in the application node set and the attribute nodes in the attribute node set according to the incidence relation to obtain the bipartite graph.
Further, the construction unit is further configured to: determining an application node in the network community to be segmented according to an application number in the application information; determining attribute nodes corresponding to each application node according to the identity information, the residence information and the communication information in the application information; determining all application nodes in the network community to be segmented as a set of the application nodes; and determining attribute nodes corresponding to all application nodes in the network community to be segmented as the set of the attribute nodes.
Further, the apparatus further comprises: and the calculating unit is used for calculating the connectivity of the attribute nodes corresponding to the application nodes contained in each target community.
Further, the apparatus further comprises: and the second determining unit is used for dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm, and determining the community to which each application node belongs by utilizing the community discovery algorithm after a community division result is obtained.
In the embodiment of the invention, the division of the network community to be divided is completed by acquiring the application information of the target service in the network community to be divided, constructing a bipartite graph of the network community to be divided according to the application information, dividing the network community to be divided into a plurality of communities, determining the number of application nodes contained in each community according to the bipartite graph, deleting the attribute nodes of which the connectivity is greater than a preset value in the target community of which the number of application nodes is greater than a first preset number in each community, finally determining the target community as the network community to be divided, and returning to execute the step of dividing the network community to be divided into the plurality of communities by using a community discovery algorithm until the number of application nodes contained in each community is less than or equal to a second preset number.
In the embodiment of the invention, the bipartite graph and the community discovery algorithm are combined to segment the large-scale network community to be segmented containing a large number of application nodes, and the large-scale network community to be segmented is segmented into a plurality of small-scale network communities containing a small number of application nodes, so that the purpose of finely segmenting the network community to be segmented is achieved, the technical problem that the nodes in the network community are difficult to analyze due to the fact that the structure of the existing network community is complex is solved, and the technical effect of reducing the difficulty in analyzing the nodes in the network community is achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a community segmentation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a bipartite graph construction method according to an embodiment of the present invention;
FIG. 3 is a flowchart of another community segmentation method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a community partitioning apparatus according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided a community segmentation method embodiment, it is noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a community segmentation method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining application information of a target service in a network community to be segmented; the application information includes at least one of: the application number, the identity information of the applicant, the living position information of the applicant and the communication information of the applicant;
it should be noted that, in this embodiment, the target service may be a banking service, and may also be a service other than the banking service, which is not specifically limited in this application.
Step S104, constructing a bipartite graph of the network community to be segmented based on the application information; the bipartite graph comprises: the system comprises an application node and/or an attribute node, wherein the application node represents an application number, and the attribute node represents at least one of identity information, living position information and communication information;
step S106, dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm to obtain a community division result; determining the number of application nodes contained in each community based on the bipartite graph;
step S108, determining target communities with the number of application nodes larger than a first preset number in a plurality of communities;
step S110, deleting the target attribute nodes with the connectivity degrees larger than a preset value in the target community; and determining the target community as a network community to be segmented, and returning to execute the step S106, namely, utilizing a community discovery algorithm to segment the network community to be segmented into a plurality of communities until the plurality of communities meet preset conditions, and taking the community segmentation result meeting the preset conditions as a target community segmentation result, wherein the preset conditions are that the number of application nodes contained in each community is less than or equal to a second preset number.
In the embodiment of the invention, the bipartite graph and the community discovery algorithm are combined to segment the large-scale network community to be segmented containing a large number of application nodes, and the large-scale network community to be segmented is segmented into a plurality of small-scale network communities containing a small number of application nodes, so that the purpose of finely segmenting the network community to be segmented is achieved, the technical problem that the nodes in the network community are difficult to analyze due to the fact that the structure of the existing network community is complex is solved, and the technical effect of reducing the difficulty in analyzing the nodes in the network community is achieved.
It should be noted that the community algorithm may adopt a Louvin algorithm, and the Louvain algorithm is an algorithm based on multi-level optimization modulation, and has the advantages of being fast and accurate, and being considered as one of the community discovery algorithms with the best performance, so that the network community to be segmented can be rapidly and accurately segmented by adopting the Louvin algorithm and the bipartite graph.
The Louvin algorithm is used for traversing all neighbor application nodes of each application node by scanning all the application nodes in the network community to be segmented, and measuring the modular yield brought by adding the application node into the community where the neighbor application node is located. And selecting the neighbor application node corresponding to the maximum profit and adding the neighbor application node into the community where the neighbor application node is located. This process repeats to guide each node's community affiliation unchanged.
And then, the formed communities are folded, and each community is folded into a single point, so that the technical effect of segmenting the communities to be segmented is achieved.
In addition, in the embodiment of the present invention, the connectivity is used to represent the connectivity of the connectivity graph, that is, the bipartite graph), and the connectivity is divided into a point connectivity and an edge connectivity, and the connectivity in the embodiment of the present invention is the point connectivity of the attribute node in the bipartite graph.
In addition, the first preset number, the second preset number, and the preset value of the connectivity may be set by a user according to an actual situation, and are not specifically limited in the embodiment of the present invention.
In the embodiment of the present invention, as shown in fig. 2, step S104 further includes the following steps:
step S11, determining a set of application nodes and a set of attribute nodes according to the application information;
step S12, determining the incidence relation between each application node in the application node set and the attribute node in the attribute node set, and establishing the incidence relation between the application node in the application node set and the attribute node in the attribute node set according to the incidence relation to obtain the bipartite graph.
In the embodiment of the present invention, in order to construct a bipartite graph of a region to be partitioned, two mutually disjoint sets (i.e., a set of application nodes and a set of attribute nodes) required for constructing the bipartite graph are first constructed according to application information obtained from a target service.
Optionally, the step of constructing the set of application nodes is as follows:
firstly, an application number in each application information is determined as an application node corresponding to each application information, and then, a set of all application nodes is determined as a set of application nodes required by the bipartite graph.
Optionally, the step of constructing the set of attribute nodes is as follows:
firstly, the identity information, the residence information and the communication information in each application information are determined as attribute nodes corresponding to each application information, and then, the set of all the attribute nodes is determined as the set of attribute nodes required by the bipartite graph.
After the application node set and the attribute node set are constructed, according to the corresponding relation between the application number and the identity information of the applicant, the living position information of the applicant and the communication information of the applicant in each piece of application information, determining the incidence relation between each application node in the application node set and the attribute node in the attribute node set, and establishing the incidence relation between the application node in the application node set and the attribute node in the attribute node set according to the incidence relation, thereby obtaining the bipartite graph of the network community to be segmented. In the bipartite graph, the association relationship may be represented by a connecting line, and other representation manners may be adopted, which is not specifically limited in the present application.
In the embodiment of the present invention, as shown in fig. 3, the method further includes the following steps:
step S109, calculating the connectivity of the attribute nodes corresponding to the application nodes contained in each target community.
In the embodiment of the present invention, in order to determine the target attribute node in step S110, after the target communities are determined, the connectivity of the attribute node corresponding to the application node included in each target community needs to be calculated.
Specifically, in this embodiment, first, application information of a target service in a network community to be segmented is obtained; then, constructing a bipartite graph of the network community to be segmented based on the application information; then, a community discovery algorithm is utilized to divide the network community to be divided into a plurality of communities, and a community division result is obtained; and determining the number of application nodes contained in each community based on the bipartite graph, calculating the connectivity of attribute nodes corresponding to the application nodes contained in each target community after determining the target communities in which the number of application nodes is greater than the first preset number in the plurality of communities, and finally executing the step S110, which is not described in detail herein.
It should be noted that, in this embodiment, the connectivity of the attribute node is obtained by the number of application nodes directly connected to the attribute node, which is a concept in graph theory. The graph is a data structure, which can be constructed by two data structures of point Edge, or can be constructed by a matrix, where the point Edge is used, the node (Vertex) includes a node number and a related attribute, and the Edge (Edge) includes an application node number and an attribute node number.
In an embodiment of the present invention, the method further includes the steps of:
step S114, the network community to be segmented is segmented into a plurality of communities by utilizing a community discovery algorithm, and after community segmentation results are obtained, the community to which each application node belongs is determined through the community discovery algorithm.
In the embodiment of the present invention, in order to determine the number of application nodes included in each community, after a bipartite graph of a network community to be partitioned is constructed, a community to which each application node belongs is determined through a community discovery algorithm, and then the application nodes included in each community are counted, so as to determine the number of application nodes included in each community.
Specifically, in this embodiment, first, application information of a target service in a network community to be segmented is obtained; then, constructing a bipartite graph of the network community to be segmented based on the application information; then, dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm, after a community division result is obtained, dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm, and after the community division result is obtained, determining the community to which each application node belongs by utilizing the community discovery algorithm; and determining the number of application nodes contained in each community based on the bipartite graph, and finally executing the steps S108 to S110, which are not described in detail herein.
The above-mentioned community segmentation method will be described with reference to a specific implementation scenario:
the above-described community segmentation method can be applied to the segmentation of credit card communities.
Firstly, obtaining a credit card application record in a credit card community to be divided, wherein the credit card application record comprises: a credit card application number, credit card applicant's identification card information, credit card applicant's residence address, credit card applicant's telephone number.
And then, determining the application number in each credit card application record as an application node corresponding to each credit card application record, and then determining the set of all application nodes as the set of application nodes required by the bipartite graph.
And determining the attribute nodes corresponding to the identity card information of the credit card applicant, the living address of the credit card applicant and the telephone number of the credit card applicant in the credit card application record, and then determining the set of all the attribute nodes as the set of the attribute nodes required by the bipartite graph.
And then, constructing a bipartite graph of the credit card community to be partitioned according to the set of the application nodes and the set of the attribute nodes, and calculating the connectivity of each attribute node.
And then, dividing the credit card community to be divided into a plurality of communities by utilizing a community discovery algorithm to obtain community division results, determining the number of application nodes contained in each community based on the bipartite graph, and determining a target community of which the number of application nodes is greater than a first preset number according to the number of application nodes contained in each community.
And finally, deleting the attribute nodes with the connectivity degree larger than a preset value in the attribute nodes corresponding to the application nodes contained in the target community.
And the step of dividing the credit card community to be divided into a plurality of communities by using the community discovery algorithm is executed again until the number of the application nodes contained in each community in the plurality of communities is less than or equal to a second preset number.
And determining the community segmentation result which meets the condition that the number of the application nodes contained in each community in the plurality of communities is less than or equal to a second preset number as a target community segmentation result, thereby achieving the purpose of segmenting the credit card community to be segmented.
Since the credit card community contains thousands of application nodes in the process of discovering the credit card community, if the undivided credit card community is used for analyzing the use records, the use flow and the like of the credit card, the analysis process is complex, and the requirement of credit card analysis service is not met.
After the credit card community is divided into a plurality of credit card communities with small application node number by adopting the community division method, the use records, the use flow and the like of the credit cards are analyzed, so that the complexity of the analysis process can be effectively reduced, and meanwhile, the use records of the credit cards of the credit card fraud groups can be determined according to the analysis result, thereby ensuring the fund security of a credit card issuing mechanism.
Example two:
the present invention further provides a community segmentation apparatus, which is configured to execute the community segmentation method provided in the foregoing embodiments of the present invention, and the following is a detailed description of the community segmentation apparatus provided in the embodiments of the present invention.
Fig. 4 is a schematic diagram of a community segmentation apparatus, which includes: an acquisition unit 10, a construction unit 20, a segmentation unit 30, a first determination unit 40 and an execution unit 50, wherein,
the acquiring unit 10 is configured to acquire application information of a target service in a network community to be partitioned; the application information includes at least one of: the application number, the identity information of the applicant, the living position information of the applicant and the communication information of the applicant;
the construction unit 20 is configured to construct a bipartite graph of the network community to be partitioned based on the application information; the bipartite graph comprises: the system comprises an application node and/or an attribute node, wherein the application node represents an application number, and the attribute node represents at least one of identity information, living position information and communication information;
the segmentation unit 30 is configured to segment the network community to be segmented into multiple communities by using a community discovery algorithm, so as to obtain a community segmentation result; determining the number of application nodes contained in each community based on the bipartite graph;
the first determining unit 40 is configured to determine, in a plurality of communities, a target community in which the number of application nodes is greater than a first preset number;
the execution unit 50 is configured to delete the target attribute node with connectivity greater than a preset value in the target community; and determining the target community as a network community to be segmented, returning to execute the step of segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm until the plurality of communities meet a preset condition, and taking a community segmentation result meeting the preset condition as a target community segmentation result, wherein the preset condition is that the number of application nodes contained in each community is less than or equal to a second preset number.
In the embodiment of the invention, the bipartite graph and the community discovery algorithm are combined to segment the large-scale network community to be segmented containing a large number of application nodes, and the large-scale network community to be segmented is segmented into a plurality of small-scale network communities containing a small number of application nodes, so that the purpose of finely segmenting the network community to be segmented is achieved, the technical problem that the nodes in the network community are difficult to analyze due to the fact that the structure of the existing network community is complex is solved, and the technical effect of reducing the difficulty in analyzing the nodes in the network community is achieved.
Preferably, the construction unit is further configured to: determining a set of application nodes and a set of attribute nodes according to the application information; and determining the incidence relation between each application node in the application node set and the attribute nodes in the attribute node set, and establishing the incidence relation between the application nodes in the application node set and the attribute nodes in the attribute node set according to the incidence relation to obtain the bipartite graph.
Preferably, the construction unit is further configured to: determining an application node in the network community to be segmented according to an application number in the application information; determining attribute nodes corresponding to each application node according to the identity information, the residence information and the communication information in the application information; determining all application nodes in the network community to be segmented as a set of the application nodes; and determining attribute nodes corresponding to all application nodes in the network community to be segmented as the set of the attribute nodes.
Preferably, the apparatus further comprises: and the calculating unit is used for calculating the connectivity of the attribute nodes corresponding to the application nodes contained in each target community.
Preferably, the apparatus further comprises: and the second determining unit is used for dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm, and determining the community to which each application node belongs by utilizing the community discovery algorithm after a community division result is obtained.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A community segmentation method, comprising:
acquiring application information of a target service in a network community to be segmented; the application information includes at least one of: the application number, the identity information of the applicant, the living position information of the applicant and the communication information of the applicant;
constructing a bipartite graph of the network community to be segmented based on the application information; the bipartite graph comprises: the system comprises an application node and/or an attribute node, wherein the application node represents an application number, and the attribute node represents at least one of identity information, living position information and communication information;
dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm to obtain a community division result; determining the number of application nodes contained in each community based on the bipartite graph;
determining target communities with application nodes more than a first preset number in a plurality of communities;
deleting the target attribute nodes with the connectivity degree larger than a preset value in the target community; and determining the target community as a network community to be segmented, returning to execute the step of segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm until the plurality of communities meet a preset condition, and taking a community segmentation result meeting the preset condition as a target community segmentation result, wherein the preset condition is that the number of application nodes contained in each community is less than or equal to a second preset number.
2. The method of claim 1, wherein constructing the bipartite graph of the network community to be partitioned based on the application information comprises:
determining a set of application nodes and a set of attribute nodes according to the application information;
and determining the incidence relation between each application node in the application node set and the attribute nodes in the attribute node set, and establishing the incidence relation between the application nodes in the application node set and the attribute nodes in the attribute node set according to the incidence relation to obtain the bipartite graph.
3. The method of claim 2, wherein determining the set of application nodes and the set of attribute nodes from the application information comprises:
determining an application node in the network community to be segmented according to an application number in the application information; determining attribute nodes corresponding to each application node according to the identity information, the residence information and the communication information in the application information;
determining all application nodes in the network community to be segmented as a set of the application nodes;
and determining attribute nodes corresponding to all application nodes in the network community to be segmented as the set of the attribute nodes.
4. The method of claim 1, further comprising:
and calculating the connectivity of the attribute nodes corresponding to the application nodes contained in each target community.
5. The method of claim 1, further comprising:
and dividing the network community to be divided into a plurality of communities by using a community discovery algorithm, and determining the community to which each application node belongs by using the community discovery algorithm after a community division result is obtained.
6. A community segmentation apparatus, comprising: an obtaining unit, a building unit, a dividing unit, a first determining unit and an executing unit, wherein,
the acquisition unit is used for acquiring application information of a target service in a network community to be segmented; the application information includes at least one of: the application number, the identity information of the applicant, the living position information of the applicant and the communication information of the applicant;
the construction unit is used for constructing a bipartite graph of the network community to be segmented based on the application information; the bipartite graph comprises: the system comprises an application node and/or an attribute node, wherein the application node represents an application number, and the attribute node represents at least one of identity information, living position information and communication information;
the segmentation unit is used for segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm to obtain a community segmentation result; determining the number of application nodes contained in each community based on the bipartite graph;
the first determining unit is used for determining target communities with application nodes more than a first preset number in a plurality of communities;
the execution unit is used for deleting the target attribute nodes with the connectivity degree larger than a preset value in the target community; and determining the target community as a network community to be segmented, returning to execute the step of segmenting the network community to be segmented into a plurality of communities by utilizing a community discovery algorithm until the plurality of communities meet a preset condition, and taking a community segmentation result meeting the preset condition as a target community segmentation result, wherein the preset condition is that the number of application nodes contained in each community is less than or equal to a second preset number.
7. The apparatus of claim 6, wherein the construction unit is further configured to:
determining a set of application nodes and a set of attribute nodes according to the application information;
and determining the incidence relation between each application node in the application node set and the attribute nodes in the attribute node set, and establishing the incidence relation between the application nodes in the application node set and the attribute nodes in the attribute node set according to the incidence relation to obtain the bipartite graph.
8. The apparatus of claim 7, wherein the construction unit is further configured to:
determining an application node in the network community to be segmented according to an application number in the application information; determining attribute nodes corresponding to each application node according to the identity information, the residence information and the communication information in the application information;
determining all application nodes in the network community to be segmented as a set of the application nodes;
and determining attribute nodes corresponding to all application nodes in the network community to be segmented as the set of the attribute nodes.
9. The apparatus of claim 6, further comprising:
and the calculation unit is used for calculating the connectivity of each attribute node in the bipartite graph.
10. The apparatus of claim 6, further comprising:
and the second determining unit is used for dividing the network community to be divided into a plurality of communities by utilizing a community discovery algorithm, and determining the community to which each application node belongs by utilizing the community discovery algorithm after a community division result is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910499900.8A CN110222241B (en) | 2019-06-10 | 2019-06-10 | Community segmentation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910499900.8A CN110222241B (en) | 2019-06-10 | 2019-06-10 | Community segmentation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110222241A CN110222241A (en) | 2019-09-10 |
| CN110222241B true CN110222241B (en) | 2021-08-31 |
Family
ID=67816229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910499900.8A Active CN110222241B (en) | 2019-06-10 | 2019-06-10 | Community segmentation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110222241B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111176838B (en) * | 2019-12-18 | 2023-12-22 | 北京百度网讯科技有限公司 | Method and device for distributing embedded vector to node in bipartite graph |
| CN111292119A (en) * | 2020-01-15 | 2020-06-16 | 杭州数澜科技有限公司 | Method and system for screening suspected illegal tissues |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107862073A (en) * | 2017-11-24 | 2018-03-30 | 山西大学 | A kind of Web community division methods based on pitch point importance and separating degree |
| US10164995B1 (en) * | 2014-08-14 | 2018-12-25 | Pivotal Software, Inc. | Determining malware infection risk |
-
2019
- 2019-06-10 CN CN201910499900.8A patent/CN110222241B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10164995B1 (en) * | 2014-08-14 | 2018-12-25 | Pivotal Software, Inc. | Determining malware infection risk |
| CN107862073A (en) * | 2017-11-24 | 2018-03-30 | 山西大学 | A kind of Web community division methods based on pitch point importance and separating degree |
Non-Patent Citations (1)
| Title |
|---|
| "自主确定社区个数的二模网络社区发现算法";郭改改等;《模式识别与人工智能》;20151130;第969-975页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110222241A (en) | 2019-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111614690B (en) | Abnormal behavior detection method and device | |
| CN109598509A (en) | The recognition methods of risk clique and device | |
| CN109271418A (en) | Suspicious clique's recognition methods, device, equipment and computer readable storage medium | |
| CN109558951A (en) | A kind of fraud account detection method, device and its storage medium | |
| CN107832407A (en) | For generating the information processing method, device and readable storage medium storing program for executing of knowledge mapping | |
| CN108429718A (en) | Account recognition methods and device | |
| CN110222241B (en) | Community segmentation method and device | |
| CN108805174A (en) | clustering method and device | |
| CN105376223A (en) | Network identity relationship reliability calculation method | |
| CN113157922B (en) | Network entity behavior evaluation and visualization method based on graph | |
| WO2023005976A1 (en) | Deep-learning-based identification method and apparatus for electric device | |
| CN109829721A (en) | Online trading multiagent behavior modeling method based on heterogeneous network representative learning | |
| CN104391879A (en) | Method and device for hierarchical clustering | |
| CN115757556A (en) | Big data based network data association analysis method and platform | |
| CN117078048A (en) | Digital twinning-based intelligent city resource management method and system | |
| CN109313541A (en) | For showing and the user interface of comparison attacks telemetering resource | |
| Cortes et al. | Giga-Mining. | |
| CN113572721B (en) | Abnormal access detection method and device, electronic equipment and storage medium | |
| CN111797942A (en) | User information classification method and device, computer equipment and storage medium | |
| Alamsyah et al. | The dynamic of banking network topology case study: Indonesian presidential election event | |
| CN111028092A (en) | Community discovery method based on Louvain algorithm, computer equipment and readable storage medium thereof | |
| CN112686654A (en) | Block chain digital currency transaction identification method and device, electronic equipment and storage medium | |
| CN112991079B (en) | Multi-card co-occurrence medical treatment fraud detection method, system, cloud end and medium | |
| JP4934660B2 (en) | Communication bandwidth calculation method, apparatus, and traffic management method | |
| CN114331665A (en) | Training method and device for credit judgment model of predetermined applicant and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |