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


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.


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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