CN112287039A - Group partner identification method and related device - Google Patents
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Abstract
The application discloses a group partner identification method, which comprises the following steps: constructing a relation map according to the acquired equipment address information to obtain an equipment relation map; constructing a relationship map according to the acquired information of the staffs at the appointed position to obtain a staff relationship map; and carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information. The relationship analysis of the group members is carried out through the device relationship map of the hardware device and the personnel relationship map of the staff member to obtain the group information, and the analysis is not carried out through the transaction relationship, so that the problem of no transaction relationship is avoided, and the accuracy of identifying the group organization is improved. The application also discloses a group partner identifying device, a server and a computer readable storage medium, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a group identification method, a group identification apparatus, a server, and a computer-readable storage medium.
Background
With the development of data technology, network data can be analyzed through the data technology so as to determine problems displayed in the data. For example, risks, especially behaviors of falsely opening criminal groups with large involved cases amount, are discovered in advance by means of data and algorithms. I.e. by analyzing the data in order to determine real-world problems implicit in the data.
In the related art, in order to identify the group hidden in the data, the invoice condition of the enterprise is firstly analyzed, and whether the enterprise has the conditions of sale or not, sale or not and the like is checked. If the conditions exist, the enterprises are checked and identified, the lower amount of money is paid for penalty, and the higher amount of money forms crimes. While another problematic business is checked and if a transaction or personal association with an existing business is found, it is assumed that they may have a group committing action. Therefore, related technologies mainly rely on the perspective of transaction relations to mine the gangs, and the actual situation is that no transaction relation exists between the gangs, and the gangs identified by the transaction relations have the situation of missing or even missing. Resulting in a lower rate of identification for the risky group organisation, leading to more security problems.
Therefore, how to improve the accuracy of identifying a group organization is a key issue of attention for those skilled in the art.
Disclosure of Invention
The application aims to provide a group partner identification method, a group partner identification device, a server and a computer readable storage medium, wherein group partner member relationship analysis is carried out through a device relationship map of hardware equipment and a personnel relationship map of an incumbent person to obtain group partner information, instead of analysis through a transaction relationship, the problem of no transaction relationship is avoided, and the accuracy of group partner organization identification is improved.
In order to solve the above technical problem, the present application provides a group partner identifying method, including:
constructing a relation map according to the acquired equipment address information to obtain an equipment relation map;
constructing a relationship map according to the acquired information of the staffs at the appointed position to obtain a staff relationship map;
and carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information.
Optionally, the relationship map is constructed according to the acquired device address information to obtain a device relationship map, including:
filtering the obtained equipment address information of the plurality of enterprises according to the white list to obtain the equipment address information to be constructed;
and constructing a relation map of the equipment address information of the target enterprise and the equipment address information of other enterprises in the equipment address information to be constructed to obtain the equipment relation map.
Optionally, the relationship map is constructed according to the acquired information of the staffs who are available, so as to obtain a staff relationship map, including:
performing relation construction on the acquired information of the staffs of the plurality of enterprises according to the staffs level to obtain a complete relation map;
and filtering the nodes of the complete relationship graph according to a preset rule to obtain the personnel relationship graph.
Optionally, performing relationship analysis processing on the device relationship graph and the personnel relationship graph by using a graph algorithm to obtain partnership information, including:
merging the equipment relation map and the personnel relation map to obtain an enterprise node relation map;
processing the enterprise node relation graph by a graph algorithm to obtain a relation analysis result;
and marking the relationship analysis result to obtain the group information.
Optionally, the processing the enterprise node relationship graph through a graph algorithm to obtain a relationship analysis result includes:
and processing the enterprise node relation map through a connected community algorithm to obtain a relation analysis result.
Optionally, the processing the enterprise node relationship graph through a graph algorithm to obtain a relationship analysis result includes:
and processing the enterprise node relation graph through a Louvain algorithm to obtain a relation analysis result.
The present application also provides a group partner identifying apparatus, including:
the hardware device relationship construction module is used for constructing a relationship map according to the acquired device address information to obtain a device relationship map;
the relation framework module of the staffs is used for constructing a relation map according to the acquired staffs information to obtain a staff relation map;
and the relation analysis module is used for carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information.
Optionally, the relationship analysis module includes:
the graph merging unit is used for merging the equipment relation graph and the personnel relation graph to obtain an enterprise node relation graph;
the graph algorithm analysis unit is used for processing the enterprise node relation graph through a graph algorithm to obtain a relation analysis result;
and the marking unit is used for marking the relationship analysis result to obtain the group information.
The present application further provides a server, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the group identification method as described above when executing the computer program.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the group identification method as described above.
The application provides a group partner identification method, which comprises the following steps: constructing a relation map according to the acquired equipment address information to obtain an equipment relation map; constructing a relationship map according to the acquired information of the staffs at the appointed position to obtain a staff relationship map; and carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information.
The equipment relation map is constructed through the obtained equipment address information, the personnel relation map is constructed through the obtained cognition personnel information, and finally the equipment relation map and the personnel relation map are subjected to corresponding relation analysis processing to obtain the identified group information instead of performing group analysis through a transaction relation, so that the condition that the group organization cannot perform relation analysis due to no transaction relation is avoided, and the accuracy and precision of identifying the group organization are improved.
The present application further provides a group partner identifying apparatus, a server, and a computer readable storage medium, which have the above beneficial effects, and are not described herein again.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a group partner identifying method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a group partner identifying device according to an embodiment of the present application.
Detailed Description
The core of the application is to provide a group partner identification method, a group partner identification device, a server and a computer readable storage medium, the relationship analysis of group partner members is carried out through an equipment relationship map of hardware equipment and a personnel relationship map of staff members to obtain group partner information, the analysis is not carried out through a transaction relationship, the problem of no transaction relationship is avoided, and the accuracy of group partner organization identification is improved.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. 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 application.
In the related art, in order to identify the group hidden in the data, the invoice condition of the enterprise is firstly analyzed, and whether the enterprise has the conditions of sale or not, sale or not and the like is checked. If the conditions exist, the enterprises are checked and identified, the lower amount of money is paid for penalty, and the higher amount of money forms crimes. While another problematic business is checked and if a transaction or personal association with an existing business is found, it is assumed that they may have a group committing action. Therefore, related technologies mainly rely on the perspective of transaction relations to mine the gangs, and the actual situation is that no transaction relation exists between the gangs, and the gangs identified by the transaction relations have the situation of missing or even missing. Resulting in a lower rate of identification for the risky group organisation, leading to more security problems.
Therefore, the method for identifying the group includes the steps that the equipment relation map is constructed through the obtained equipment address information, the personnel relation map is constructed through the obtained cognition personnel information, and finally the equipment relation map and the personnel relation map are subjected to corresponding relation analysis processing to obtain the identified group information instead of performing group analysis through a transaction relation, so that the situation that the group organization cannot perform relation analysis due to the fact that the group organization does not have the transaction relation is avoided, and accuracy and precision of identifying the group organization are improved.
The following describes a group identification method provided by the present application by an embodiment.
Referring to fig. 1, fig. 1 is a flowchart illustrating a group partner identification method according to an embodiment of the present disclosure.
In this embodiment, the method may include:
s101, constructing a relation map according to the acquired equipment address information to obtain an equipment relation map;
the method comprises the steps of constructing a relation map according to the acquired equipment address information to obtain an equipment relation map. Mainly, the information data adopted in the related technology is the information data related to the transaction, and in the actual situation, the occurrence of the transaction is intentionally avoided among partial groups, so that the equipment address information in the enterprise is constructed in the step, and the relationship analysis among all organization members is further carried out through the equipment relationship graph.
Wherein, on the basis of the acquired device address information of the target enterprise, namely the seed enterprise. In this step, any relationship construction method provided in the prior art may be referred to, and is not specifically limited herein.
In order to further acquire a more accurate device relationship map, the method may include:
step 1, filtering the acquired equipment address information of a plurality of enterprises according to a white list to obtain the equipment address information to be constructed;
and 2, constructing a relation map of the equipment address information of the target enterprise and the equipment address information of other enterprises in the equipment address information to be constructed to obtain an equipment relation map.
Therefore, how to obtain the device relationship map is mainly explained in the alternative. In the alternative scheme, firstly, the obtained equipment address information of a plurality of enterprises is filtered according to a white list to obtain the equipment address information to be constructed; the normal device addresses recorded by the white list can be excluded in the relationship construction, so that the device relationship map is prevented from being influenced. And then, constructing a relation map of the equipment address information of the target enterprise and the equipment address information of other enterprises in the equipment address information to be constructed to obtain an equipment relation map.
S102, constructing a relation map according to the acquired information of the staffs of the job to obtain a staff relation map;
the method comprises the steps of constructing a relation map according to acquired information of the staffs at the appointed position to obtain a staff relation map. Mainly because the information data adopted in the related technology is the information data related to the transaction, and the occurrence of the transaction is intentionally avoided among partial groups in the actual situation, the information of the staffs in the enterprise is constructed in the step, and the relationship analysis among all organization members is carried out through the information of the staffs.
Wherein, on the basis of the acquired information of the target enterprise, namely the designated personnel of the seed enterprise. In this step, any relationship construction method provided in the prior art may be referred to, and is not specifically limited herein.
In order to further acquire an accurate personnel relationship map, the method comprises the following steps:
step 1, performing relationship construction on the acquired information of the staffs of a plurality of enterprises according to the staffs level to obtain a complete relationship map;
and 2, filtering the nodes of the complete relationship graph according to a preset rule to obtain the personnel relationship graph.
Therefore, how to acquire the personnel relationship map is mainly explained in the alternative. According to the alternative scheme, firstly, the relationship construction is carried out on the acquired information of the staffs of the enterprises according to the staffs grades, and a complete relationship map is obtained. Namely, no matter how the relation of each person in the information of the staffs of the job, a complete relation map is constructed for the staffs of the job. And then, filtering the nodes of the complete relationship graph according to a preset rule to obtain the personnel relationship graph. Wherein the preset rules include, but are not limited to, tax clerks, financial responsible persons, time of employment, and time of employment.
And S103, carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information.
On the basis of S101 and S102, the step aims to perform relationship analysis processing on the equipment relationship map and the personnel relationship map by adopting a graph algorithm to obtain group information. That is, on the basis of the constructed equipment relationship graph and the personnel relationship graph, the partnership organization in the equipment relationship graph and the personnel relationship graph can be determined by carrying out corresponding graph algorithm on the node relationship of the two graphs.
The graph algorithm is a simple algorithm for obtaining answers by using a special line arithmetic graph. Undirected graphs, directed graphs, and networks can employ many commonly used graph algorithms, including: various traversal algorithms, algorithms to find shortest paths, algorithms to find lowest cost paths in the network, algorithms to answer some simple related questions (e.g., whether a graph is connected, what is the shortest path between two vertices in the graph, etc.). The graph algorithm can be applied to a variety of scenarios, for example: and optimizing pipelines, routing tables, express services, communication websites and the like.
Further, any operation manner of the graph algorithm provided in the prior art may be adopted in this step, which is not specifically limited herein.
Optionally, to further explain how to perform the relationship analysis and improve the accuracy of the group information, the step may include:
step 1, combining an equipment relation map and a personnel relation map to obtain an enterprise node relation map;
step 2, processing the enterprise node relation map through a map algorithm to obtain a relation analysis result;
and 3, marking the relation analysis result to obtain the group information.
It can be seen that the present alternative solution mainly explains how to obtain the group information. In the alternative scheme, the equipment relation map and the personnel relation map are combined to obtain an enterprise node relation map; specifically, the equipment relationship map and the personnel relationship map are combined and imported into a map database to construct a relationship map with complete enterprise, equipment relationship and personnel relationship and an enterprise node relationship map. The nodes are hardware devices and staffs. Then, processing the enterprise node relation graph through a graph algorithm to obtain a relation analysis result; and finally, marking the relation analysis result to obtain the group information.
Optionally, step 3 in the last alternative may include:
and processing the enterprise node relation map through a connected community algorithm to obtain a relation analysis result.
The connected community algorithm is divided into a weak connected community algorithm and a strong connected community algorithm, and is one of common graph algorithms. All nodes in the weakly connected or undirected graph can reach other nodes through one path, and all nodes in the strongly connected community or the directed graph can reach other nodes through one path.
Optionally, step 3 in the last alternative may include:
and processing the enterprise node relation graph through a Louvain algorithm to obtain a relation analysis result.
Among them, the Louvain algorithm, also called Fast Unfolding algorithm, is a typical graph clustering algorithm based on modularity. The modularity can also be understood as the sum of the weights of all edges connected with community nodes subtracted from the weight of the internal edges of the community, the community division aims to enable the internal connection of the divided community to be tight, the connection between communities is sparse, the advantages and disadvantages of the division can be described through the modularity, and the larger the modularity is, the better the community division effect is.
In summary, in this embodiment, the device relationship map is constructed by the obtained device address information, the person relationship map is constructed by the obtained acquirer information, and finally, the device relationship map and the person relationship map are subjected to corresponding relationship analysis processing to obtain the identified group information, instead of performing group analysis by using a transaction relationship, so that a situation that the group organization cannot perform relationship analysis due to no transaction relationship is avoided, and the accuracy and precision of identifying the group organization are improved.
The group identification method provided by the present application is further described below by a specific embodiment.
In this embodiment, the method may include:
step 1, inputting a seed enterprise, namely a target enterprise. The enterprise determined as the false open is used as a seed enterprise, and the feedback time for finding the false open of the enterprise is input together, and the source of the seed enterprise is the enterprise which is actually in the false open problem after being checked by the tax inspection department.
And step 2, determining the MAC Address of the seed enterprise MAC. And finding the mac address through invoice information issued by the seed enterprise, and simultaneously removing the mac address in the white list of the mac address. The mac address white list refers to the mac address which is a public address or a mac address of more than 1000 opened enterprises. These addresses are typically normal addresses and need to be culled.
And 3, constructing a map of mac address relation. And associating the mac address of the seed enterprise with the mac addresses of all other non-seed enterprises to construct a map of the mac address relationship.
And 4, constructing a relation map of the staffs of the job. And associating other non-seed enterprises which are the same as the five kinds of the incumbent personnel of the seed enterprise through the five kinds of the incumbent personnel of the seed enterprise to form an incumbent personnel relationship map. The five kinds of staffs are: corporate legal, investor, financial responsible, ticketing and tax clerks.
And 5, filtering graph nodes of the staffs. The specific business rules are as follows:
tax clerks: { number of incumbent business users <50} or { number of incumbent business users >50 and (incumbent business abnormal status + abnormal logout status taxpayer)/all incumbent businesses > 50% }. Mainly aims to prevent the account agency in normal operation from being misjudged.
The financial responsible person: { number of incumbent business households <50} or { number of incumbent business households >50 and (incumbent business abnormal status + abnormal logout status taxpayer)/all incumbent businesses > 50% }. Mainly aims to prevent the account agency in normal operation from being misjudged.
And when the enterprise is determined to be in the false start, 5 types of personnel still hold the job by pushing forward for 3 months, otherwise, the personnel relationship is not included in the personnel relationship construction range. And the time determined as the false opening by the enterprise is the response feedback recording time. If the feedback time is not responded, the current month is pushed forward by 6 months and still works, for example, the current month is 12 months, and then the 6 months are still worked. Mainly for guaranteeing the validity of the data of the staff.
And 6, combining the mac address and the relationship map of the staffs, and importing the map into a map database. And constructing a relation map with complete enterprises, mac addresses and staffs.
And 7, mining the gangs by applying a graph algorithm. The invention uses two graph algorithms, namely a connected community algorithm and a louvain algorithm.
Graph algorithms, which are one of the tools for graph analysis, provide the most efficient way to analyze connected data, describing how to process graphs to find some qualitative or quantitative conclusions. Graph algorithms are based on graph theory, and use relationships between nodes to infer structure and variations of complex systems. These algorithms can be used to discover hidden information, validate business hypotheses, and predict behavior. Common algorithms include a connected community algorithm, a luvain modularity clustering algorithm and the like.
The connected community algorithm is divided into a weak connected community algorithm and a strong connected community algorithm, and is one of common graph algorithms. All nodes in the weakly connected or undirected graph can reach other nodes through one path, and all nodes in the strongly connected community or the directed graph can reach other nodes (output standard ring loops) through one path. The present embodiment uses the weak connected community algorithm. Usually, a huge community is obtained, and several other small island communities. Such as: a connected community algorithm is input into a region layout of China to perform region division, and output results are continents and small islands.
Among them, the louvain algorithm, also called Fast Unfolding algorithm, is a typical graph clustering algorithm based on modularity. The modularity can also be understood as the sum of the weights of all edges connected with community nodes subtracted from the weight of the internal edges of the community, the community division aims to enable the internal connection of the divided community to be tight, the connection between communities is sparse, the advantages and disadvantages of the division can be described through the modularity, and the larger the modularity is, the better the community division effect is.
The Fast Unfolding algorithm is an algorithm for dividing communities based on modularity, and is an iterative algorithm, and the main aim is to continuously divide communities so that the modularity of the divided whole network is continuously increased. The method mainly comprises two stages, wherein the first stage is called modulation Optimization and mainly comprises the steps of dividing each node into communities where nodes adjacent to the node are located, so that the value of Modularity is continuously increased; the second stage is called Community Aggregation, which mainly aggregates the communities divided in the first step into one point, i.e. reconstructing a network according to the Community structure generated in the previous step. The above process is repeated until the structure in the network is no longer changed.
The two algorithms of the invention are realized in the following concrete steps:
and (3) dividing the large relational graph into a plurality of connected subgraphs by using a connected community algorithm, further dividing the plurality of connected subgraphs by using a louvain algorithm, and then obtaining a final group result, namely 3 groups.
And 8, marking the acquired group enterprise. Businesses that are the same group are marked and all identified groups are directed into a graph database for use invocation.
Therefore, in the embodiment, the equipment relationship map is constructed through the obtained equipment address information, the personnel relationship map is constructed through the obtained acquirement personnel information, and finally, the equipment relationship map and the personnel relationship map are subjected to corresponding relationship analysis processing to obtain the identified group information, instead of performing group analysis through a transaction relationship, so that the situation that the group organization cannot perform relationship analysis due to no transaction relationship is avoided, and the accuracy and precision of identifying the group organization are improved.
The embodiments of the present application provide a group partner identifying device, and the group partner identifying device described below and the group partner identifying method described above may be referred to in correspondence with each other.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a group partner identifying apparatus according to an embodiment of the present application.
In this embodiment, the apparatus may include:
the hardware device relationship building module 100 is configured to build a relationship map according to the obtained device address information to obtain a device relationship map;
the relationship construction module 200 is used for constructing a relationship map according to the acquired information of the staffs to obtain a staff relationship map;
and the relationship analysis module 300 is configured to perform relationship analysis processing on the equipment relationship map and the personnel relationship map by using a graph algorithm to obtain group information.
Optionally, the hardware device relationship building module 100 may include:
the device information acquisition unit is used for filtering the acquired device address information of the plurality of enterprises according to the white list to obtain device address information to be constructed;
and the equipment map construction unit is used for constructing a relation map of the equipment address information of the target enterprise and the equipment address information of other enterprises in the equipment address information to be constructed to obtain an equipment relation map.
Optionally, the job personnel relationship framework module 200 may include:
the personnel relationship integrity construction unit is used for constructing relationships of the acquired personnel information of the duties of the plurality of enterprises according to the duties grades to obtain an integral relationship map;
and the map node filtering unit is used for filtering the nodes of the complete relation map according to a preset rule to obtain the personnel relation map.
Optionally, the relationship analysis module 300 may include:
the map merging unit is used for merging the equipment relation map and the personnel relation map to obtain an enterprise node relation map;
the graph algorithm analysis unit is used for processing the enterprise node relation graph through a graph algorithm to obtain a relation analysis result;
and the marking unit is used for marking the relationship analysis result to obtain the group information.
Optionally, the graph algorithm processing unit is specifically configured to process the enterprise node relationship graph through a connected community algorithm to obtain a relationship analysis result.
Optionally, the graph algorithm processing unit is specifically configured to process the enterprise node relationship graph through a Louvain algorithm to obtain a relationship analysis result.
An embodiment of the present application further provides a server, including:
a memory for storing a computer program;
a processor for implementing the steps of the group recognition method as described in the above embodiments when executing the computer program.
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the group partner identifying method according to the above embodiments.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
A group identification method, a group identification apparatus, a server and a computer readable storage medium provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (10)
1. A group partner identification method, comprising:
constructing a relation map according to the acquired equipment address information to obtain an equipment relation map;
constructing a relationship map according to the acquired information of the staffs at the appointed position to obtain a staff relationship map;
and carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information.
2. The group partner identifying method according to claim 1, wherein the step of performing relationship map construction according to the obtained device address information to obtain a device relationship map comprises:
filtering the obtained equipment address information of the plurality of enterprises according to the white list to obtain the equipment address information to be constructed;
and constructing a relation map of the equipment address information of the target enterprise and the equipment address information of other enterprises in the equipment address information to be constructed to obtain the equipment relation map.
3. The partnership identification method according to claim 1, wherein the relationship map construction is performed according to the acquired information of the incumbent personnel to obtain a personnel relationship map, and the method comprises the following steps:
performing relation construction on the acquired information of the staffs of the plurality of enterprises according to the staffs level to obtain a complete relation map;
and filtering the nodes of the complete relationship graph according to a preset rule to obtain the personnel relationship graph.
4. The partnership identification method according to claim 1, wherein the relationship analysis processing is performed on the equipment relationship map and the personnel relationship map by using a graph algorithm to obtain partnership information, and the method comprises the following steps:
merging the equipment relation map and the personnel relation map to obtain an enterprise node relation map;
processing the enterprise node relation graph by a graph algorithm to obtain a relation analysis result;
and marking the relationship analysis result to obtain the group information.
5. The group partner identifying method according to claim 1, wherein the processing the enterprise node relationship graph through a graph algorithm to obtain a relationship analysis result comprises:
and processing the enterprise node relation map through a connected community algorithm to obtain a relation analysis result.
6. The group partner identifying method according to claim 1, wherein the processing the enterprise node relationship graph through a graph algorithm to obtain a relationship analysis result comprises:
and processing the enterprise node relation graph through a Louvain algorithm to obtain a relation analysis result.
7. A group partner identifying apparatus, comprising:
the hardware device relationship construction module is used for constructing a relationship map according to the acquired device address information to obtain a device relationship map;
the relation framework module of the staffs is used for constructing a relation map according to the acquired staffs information to obtain a staff relation map;
and the relation analysis module is used for carrying out relation analysis processing on the equipment relation map and the personnel relation map by adopting a map algorithm to obtain group information.
8. The group partner identifying device of claim 1, wherein the relationship analysis module comprises:
the graph merging unit is used for merging the equipment relation graph and the personnel relation graph to obtain an enterprise node relation graph;
the graph algorithm analysis unit is used for processing the enterprise node relation graph through a graph algorithm to obtain a relation analysis result;
and the marking unit is used for marking the relationship analysis result to obtain the group information.
9. A server, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the group identification method as claimed in any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the group identification method according to any one of claims 1 to 6.
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| CN (1) | CN112287039A (en) |
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| CN113506113A (en) * | 2021-06-02 | 2021-10-15 | 北京顶象技术有限公司 | Credit card cash-registering group-partner mining method and system based on associated network |
| CN114003648A (en) * | 2021-10-20 | 2022-02-01 | 支付宝(杭州)信息技术有限公司 | Risk transaction group partner identification method and device, electronic equipment and storage medium |
| CN114003648B (en) * | 2021-10-20 | 2024-04-26 | 支付宝(杭州)信息技术有限公司 | Identification method and device for risk transaction group partner, electronic equipment and storage medium |
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| CN113506113A (en) * | 2021-06-02 | 2021-10-15 | 北京顶象技术有限公司 | Credit card cash-registering group-partner mining method and system based on associated network |
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