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Weighted network community clustering method based on hybrid measure

A technology of weighted network and clustering method, which is applied in the field of data mining and complex network analysis, can solve problems such as extreme degradation, inability to find communities with a size smaller than a certain size, and resolution limitations, so as to reduce scale, alleviate extreme degradation problems, The effect of high clustering quality

Inactive Publication Date: 2016-01-13
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Application Information

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Problems solved by technology

However, there are resolution limit and extreme degeneration problems in modularity optimization.
The resolution-limited problem means that algorithms using modularity optimization cannot find communities with a size smaller than a certain size

Method used

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  • Weighted network community clustering method based on hybrid measure
  • Weighted network community clustering method based on hybrid measure
  • Weighted network community clustering method based on hybrid measure

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Embodiment Construction

[0032] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.

[0033] figure 1 It is a flowchart of community structure discovery in the present invention, and its main steps include:

[0034] 1. Initialize the network:

[0035] Abstract a specific network as a graph G=(V,E) composed of point set V and edge set E, using matrix A ij Represents the connection relationship between network nodes; the number of nodes in graph G=(V,E) is n, and its nodes are marked as v in turn i (i=1,2,3...n); if node v i and v j There is an edge connection between them, then A ij = 1, otherwise A ij =0.

[0036] The degree of a node is the number of edges associated with a node.

[0037] If graph G is an undirected graph, node v i The degrees are expressed as follows:

[0038] k i =∑ j A ij ;

[0039] If graph G is a directed graph, node v i Th...

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Abstract

The present invention discloses a weighted network community clustering method based on hybrid measure to dig the clustering relationship of nodes in a large complex network. The method comprises a step of introducing a new node intimacy definition for measuring the association intensity between the nodes in a directed weighted network, a step of carrying out weighted processing on the side of a directed / undirected network through newly defined node intimacy, and a step of providing a modular new definition based on the node intimacy and using the hybrid measure to carry out hierarchical community structure detection on the directed / undirected network. Compared with the traditional community structure detection method, node relation information which can be referred in the communication division is increased by the hybrid measure, the quality of the communication division is improved, and the scale of an ultra large community is reduced. At the same time, the method provides a unified analysis method for the communication division of undirected unweighted, directed unweighted, undirected weighted and directed unweighted networks.

Description

technical field [0001] The invention relates to the fields of data mining and complex network analysis, in particular to a community structure detection method based on mixed measures in large-scale weighted social networks. technical background [0002] With the widespread application of social networks based on the Internet, more and more people join in social networks for information exchange activities. The application of social networks has changed the way people create, disseminate and use information. At the same time, the scale of users in social networks and the information produced are also increasing rapidly. According to BusinessInsider, the number of users of Facebook, the world's largest social networking site, has exceeded 2.2 billion on July 25, 2014, accounting for 1 / 3 of the world's total population. The number of active users of Sina Weibo reached 167 million on September 30, 2014, and more than 100 million new Weibo messages were added every day. [00...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06F17/30
Inventor 刘瑶刘峤秦志光其他发明人请求不公开姓名
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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