Fast community detection for dynamic complex networks

Shweta Bansal, Sanjukta Bhowmick, Prashant Paymal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations


Dynamic complex networks are used to model the evolving relationships between entities in widely varying fields of research such as epidemiology, ecology, sociology, and economics. In the study of complex networks, a network is said to have community structure if it divides naturally into groups of vertices with dense connections within groups and sparser connections between groups. Detecting the evolution of communities within dynamically changing networks is crucial to understanding complex systems. In this paper, we develop a fast community detection algorithm for real-time dynamic network data. Our method takes advantage of community information from previous time steps and thereby improves efficiency while maintaining the quality of community detection. Our experiments on citation-based networks show that the execution time improves as much as 30% (average 13%) over static methods.

Original languageEnglish (US)
Title of host publicationComplex Networks - Second International Workshop, CompleNet 2010, Revised Selected Papers
Number of pages12
StatePublished - 2011
Externally publishedYes
Event2nd International Workshop on Complex Networks, CompleNet 2010 - Rio de Janeiro, Brazil
Duration: Oct 13 2010Oct 15 2010

Publication series

NameCommunications in Computer and Information Science
Volume116 CCIS
ISSN (Print)1865-0929


Conference2nd International Workshop on Complex Networks, CompleNet 2010
CityRio de Janeiro

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


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