Fast Community Detection for Dynamic Complex Networks

  • Shweta Bansal
  • Sanjukta Bhowmick
  • Prashant Paymal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 116)


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.


Community Detection Previous Time Step Dynamic Algorithm Dynamic Complex Network Community Detection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shweta Bansal
    • 1
  • Sanjukta Bhowmick
    • 2
  • Prashant Paymal
    • 2
  1. 1.Center for Infectious Disease DynamicsPenn State UniversityUniversity ParkUSA
  2. 2.Department of Computer ScienceUniversity of Nebraska at OmahaOmahaUSA

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