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Structure centrality-based network community discovery method

A discovery method and network community technology, applied in the field of network community discovery based on structural centrality, can solve problems such as ignoring node roles and failing to reveal community structure, so as to avoid unreasonable division and improve rationality

Inactive Publication Date: 2017-04-26
SHANGHAI JIAO TONG UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

In addition, although some local methods have been proposed, these local community discovery methods cannot reveal the inherent community structure while ignoring the different node roles in the community

Method used

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  • Structure centrality-based network community discovery method
  • Structure centrality-based network community discovery method
  • Structure centrality-based network community discovery method

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Embodiment Construction

[0055] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0056] The invention discloses a network community discovery method based on structural centrality. This method can effectively reveal the inherent community structure in complex networks. According to the local interaction information of nodes and the global information between nodes, a node centrality measure is proposed to locate the local structural center nodes in the network and determine the number of community clusters at the same time; based on the identified structural centers, the present inv...

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Abstract

The invention provides a structure centrality-based network community discovery method, which can effectively reveal inherent community structures in a complex network. A node centrality measure is proposed for locating local structure center nodes in the network according to local interaction information of nodes and global information among the nodes, and a community cluster quantity is determined; and based on an identified structure center, a complete community structure is revealed in a way of extension to the edge from the structure center by adopting a heuristic local search policy. Meanwhile, a boundary region is defined for the community structure, so that different node roles in a network community are effectively identified for revealing network group structure characteristics. A large amount of experiments prove that the method can effectively reveal the community structure in the network, is relatively high in universality and has a relatively high application value.

Description

technical field [0001] The invention belongs to the field of complex network analysis technology research. Specifically, it involves a network community discovery method based on structural centrality. Background technique [0002] A complex network is an abstract description of a complex system. Nodes in the network represent individuals in the complex system, and edges in the network represent the internal connections between individuals in the complex system. Complex network has become a broad interdisciplinary subject, involving physics, biology, social science, system science, network science and other disciplines, and has gradually become a challenging research topic to solve complex problems, and has a wide range of applications in many fields, such as Social network analysis, bioengineering, electricity and transportation, human behavior analysis, big data analysis, etc. The research and analysis of complex networks has greatly expanded the breadth and depth of peo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q50/00H04L12/24
CPCG06F16/958G06Q50/01H04L41/14
Inventor 潘理汪晓锋李建华
Owner SHANGHAI JIAO TONG UNIV
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