Scalably revealing the dynamics of soft community structure in complex networks
- 85 Downloads
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.
KeywordsCommunity detection dynamical behavior Markov process Potts model soft partition
Unable to display preview. Download preview PDF.
- Von Luxburg U, A tutorial on spectral clustering, Tech. Rep., 2006, 149, Max Planck Institute for Biological Cybernetics.Google Scholar
- Bach F R and Jordan M I, Advances in Neural Information Processing Systems, NIPS*, Eds. by Thrun S, Saul L, and Schoelkopf B, MIT Press, Cambridge, MA, 2003, 16.Google Scholar
- Zha H, He X, Ding C, Gu M, and Simon H, Spectral relaxation for k-mean, NIPS*, 2001, 14: 1057–1064.Google Scholar
- Shi J and Malik J, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22: 8888.Google Scholar