Link community detection based on line graphs with a novel link similarity measure

Guishen Wang, Lan Huang, Yan Wang, Wei Pang, Qin Ma

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Link community gradually unfolds its capacity in complex network research. In this paper, a novel link similarity measure on line graphs is proposed. This measure can be adapted to different types of networks with an adjustable parameter. We prove its value converges to a limit on line graphs with the relationship of the nonneighbor links taken into account. Based on this similarity measure, we propose a novel link community detection algorithm for link clustering on line graphs. The detection algorithm combines the novel link similarity measure with the classic Markov Cluster (MCL) Algorithm and determines the link community partitions by calculating an extended modularity measure. Extensive experiments on two types of complex networks demonstrate the effectiveness, reliability and rationality of our solution in contrast to the other two classical algorithms.


Read More: http://www.worldscientific.com/doi/abs/10.1142/S0217979216500235?src=recsys
Original languageEnglish
Article number1650023
Number of pages18
JournalInternational Journal of Modern Physics B
Volume30
Issue number6
Early online date2 Feb 2016
DOIs
Publication statusPublished - 10 Mar 2016

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Keywords

  • link community detection
  • Markov cluster algorithm
  • line graphs
  • overlapping community detection
  • link similarity
  • complex networks

Cite this

Link community detection based on line graphs with a novel link similarity measure. / Wang, Guishen; Huang, Lan; Wang, Yan; Pang, Wei; Ma, Qin.

In: International Journal of Modern Physics B, Vol. 30, No. 6, 1650023, 10.03.2016.

Research output: Contribution to journalArticle

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