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Journal of Systems Science and Complexity

, Volume 29, Issue 4, pp 1071–1088 | Cite as

Scalably revealing the dynamics of soft community structure in complex networks

  • Huijia LiEmail author
  • Huiying Li
Article

Abstract

Revealing the dynamics of community structure is of great concern for scientists from many fields. Specifically, how to quantify the dynamic details of soft community structure is a very interesting topic. In this paper, the authors propose a novel framework to study the scalable dynamic behavior of the soft community structure. First, the authors model the Potts dynamics to detect community structure using a “soft” Markov process. Then the soft stability of in a multiscale view is proposed to naturally uncover the local uniform behavior of spin values across multiple hierarchical levels. Finally, a new partition index is developed to detect fuzzy communities based on the stability and the dynamical information. Experiments on the both synthetically generated and real-world networks verify that the framework can be used to uncover hierarchical community structures effectively and efficiently.

Keywords

Community detection dynamical behavior Markov process Potts model soft partition 

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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Management Science and EngineeringCentral University of Finance and EconomicsBeijingChina
  2. 2.Department of AutomationTsinghua UniversityBeijingChina

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