Dynamic evolutionary community detection algorithms based on the modularity matrix

doi:10.1088/1674-1056/23/11/118903

Abstract Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms.

Keywords: community detection dynamic evolutionary modularity matrix synchronization

Received: 31 January 2014
Revised: 01 June 2014
Accepted manuscript online:

PACS:	89.75.Fb	(Structures and organization in complex systems)
	89.75.Hc	(Networks and genealogical trees)
	84.30.Bv	(Circuit theory)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61272279), the TianYuan Special Funds of the National Natural Science Foundation of China (Grant No. 11326239), the Higher School Science and Technology Research Project of Inner Mongolia, China (Grant No. NJZY13119), and the Inner Mongolia University of Technology, China (Grant No. ZD201221).

Corresponding Authors: Chen Jian-Rui
E-mail: jianrui_chen@sina.com

Cite this article:

Chen Jian-Rui (陈建芮), Hong Zhi-Min (洪志敏), Wang Li-Na (汪丽娜), Wu Lan (乌兰) Dynamic evolutionary community detection algorithms based on the modularity matrix 2014 Chin. Phys. B 23 118903

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