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Scalably revealing the dynamics of soft community structure in complex networks

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.

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Authors

Corresponding author

Correspondence to Huijia Li.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 71401194, 91324203 and 11131009, and “121” Youth Development Fund of CUFE under Grant No. QBJ1410.

This paper was recommended for publication by Editor DI Zengru.

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Li, H., Li, H. Scalably revealing the dynamics of soft community structure in complex networks. J Syst Sci Complex 29, 1071–1088 (2016). https://doi.org/10.1007/s11424-015-4145-6

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  • DOI: https://doi.org/10.1007/s11424-015-4145-6

Keywords

  • Community detection
  • dynamical behavior
  • Markov process
  • Potts model
  • soft partition