基于活跃点的社区跟踪算法

doi:10.3969/j.issn.0255-8297.2017.05.006

Abstract

Abstract:

The research of complex networks is mainly aimed at the complex systems, and it is a general method for dealing with various problems in complex systems. In general, complex network community tracking neglects evolutionary time domain factors and differences in the evolution of network members. This paper proposes a community tracking method that includes time domain information in the similarity function, and extracts active nodes in the network by taking into account smoothness of network evolution and differences between nodes. Experiments show that the proposed algorithm fnds the community evolution process better than those based on DBLP data sets. It can also discover community similarity effectively.

Key words: community evolution, social network analysis, activity point, temporary networks, community track

CLC Number:

TP301.6

YANG Shao-wen, YAN Guang-hui, LI Lei, ZHANG Hai-tao. Community Tracking Algorithm Based on Active Points[J]. Journal of Applied Sciences, 2017, 35(5): 602-611.

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Community Tracking Algorithm Based on Active Points

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