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Relation graph key personnel analysis method and system based on graph embedding

An analysis method and graph embedding technology, applied in the field of knowledge graph analysis, can solve the problems of low accuracy, low accuracy, and the lack of scalability of key person mining methods, so as to achieve accurate analysis results and eliminate adverse effects.

Pending Publication Date: 2022-08-09
GRG BAKING EQUIP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of the above technical problems, the purpose of the present invention is to provide a method and system for analyzing key personnel in relational graphs based on graph embedding, so as to solve the problem that the traditional key person mining method does not have scalability, or the weight update of node personnel only includes Local structural information and personnel information lead to low accuracy, or rules that rely on direct module and indirect modularity gain lead to low accuracy

Method used

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  • Relation graph key personnel analysis method and system based on graph embedding
  • Relation graph key personnel analysis method and system based on graph embedding
  • Relation graph key personnel analysis method and system based on graph embedding

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Embodiment 1

[0042] First, the technical terms in the present invention will be explained below:

[0043]Graph Embedding (also called Network Embedding) is a process of mapping graph data (usually a high-dimensional dense matrix) into a low-density vector, which can well solve the problem that graph data is difficult to efficiently input into machine learning algorithms.

[0044] The adjacency matrix is ​​a matrix that represents the adjacent relationship between vertices. The logical structure of the adjacency matrix is ​​divided into two parts: V and E sets, where V is a vertex and E is an edge. Therefore, a one-dimensional array is used to store all vertex data in the graph; a two-dimensional array is used to store the data of the relationship between vertices (edges or arcs). This two-dimensional array is called an adjacency matrix.

[0045] Centrality is used to measure the importance of nodes in the network. Centrality can be defined for a single node or a group of multiple nodes. ...

Embodiment 3

[0090] Figure 4 A schematic structural diagram of an electronic device provided in the embodiment of this application, in this application, can be Figure 4 The schematic diagram shown is used to describe the electronic device 100 for implementing a graph embedding-based key person analysis method for a relational graph of the present invention.

[0091] As shown in FIG. 4 is a schematic structural diagram of an electronic device, the electronic device 100 includes one or more processors 102 and one or more storage devices 104. These components are connected through a bus system and / or other forms of connection mechanisms (not shown). out) interconnection. It should be noted that Figure 4 The illustrated components and structures of the electronic device 100 are only exemplary and not limiting, and the electronic device may have Figure 4 shown in some components, which can also have Figure 4 Other components and structures not shown.

[0092] The processor 102 may be a ...

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Abstract

The invention discloses a relation graph key personnel analysis method and system based on graph embedding. The method comprises the following steps: constructing a character relation graph based on social media data; analyzing each node in the figure relation graph by adopting a graph embedding algorithm to obtain an embedding vector of each node; generating a key node seed of the character relation graph according to a pre-related index; and analyzing the key node seeds by adopting a clustering algorithm according to the embedded vector of each node, and identifying key personnel nodes. According to the method, the topological property of the relation graph is fully utilized, learnability is achieved, and parameter values or calculation rules for specifying degree gains do not need to be manually set, so that the adverse effect on the result caused by unreasonable manual rule setting is eliminated; meanwhile, the whole graph is calculated, and the isomorphism and heterogeneity of nodes are integrated, so that the obtained key personnel analysis result is more accurate.

Description

technical field [0001] The invention relates to the technical field of knowledge graph analysis, in particular to a method and system for analyzing key personnel of a relation graph based on graph embedding. Background technique [0002] The person relationship graph is a knowledge graph constructed with the social, kinship, and emotional relationship between the "person" entity and the person as the core. According to the "Six Degrees of Separation Theory", in interpersonal communication, any two strangers can establish contact through at most five friends. To some extent, all people in the world can be connected by personal networks. Because of the complexity of the real world, there are more and more types of characters and relationships involved in the construction of relational graphs. In several sub-graphs of a relationship graph, only one character or several characters play the main role, especially in public opinion analysis, administrative management, risk contro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/28G06K9/62G06F17/18
CPCG06F16/367G06F16/288G06F16/285G06F17/18G06F18/2414G06F18/23213G06F18/22G06F18/2415
Inventor 张暐郭峰陈瀚平曹瑞雪陈栩琪
Owner GRG BAKING EQUIP CO LTD
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