Fuzzy analysis for overlapping community structure of complex network

Kun Zhao, Shao Wu Zhang, Quan Pan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

We consider the problem of fuzzy community detection in networks, which complements the concept of overlapping community structure. Using the optimization method to approximate network feature matrix is an important approach for conventional fuzzy community detection. In order to retain valuable physical meaning of the approximation, we discard redundant constraints in the process of approximation which is accordingly reduced to a problem of symmetrical non-negative matrix factorization (s-NMF). The resulting fuzzy metric, which is termed clique-node similarity degree (CNSD), is able to grasp very subtle topology information of the node's neighborhood. Based on the CNSD, we introduce a new measure that is able to identify the key nodes that are critical to the connection of the adjacent communities. The technique is able to discover the fuzzy community structure of different real world networks with high confidence.

Original languageEnglish
Title of host publication2010 Chinese Control and Decision Conference, CCDC 2010
Pages3976-3981
Number of pages6
DOIs
StatePublished - 2010
Event2010 Chinese Control and Decision Conference, CCDC 2010 - Xuzhou, China
Duration: 26 May 201028 May 2010

Publication series

Name2010 Chinese Control and Decision Conference, CCDC 2010

Conference

Conference2010 Chinese Control and Decision Conference, CCDC 2010
Country/TerritoryChina
CityXuzhou
Period26/05/1028/05/10

Keywords

  • Clique-node similarity degree
  • Inter-Clique connecting contribution
  • Overlapping community structure
  • Symmetrical non-negative factorization

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