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Potts model based on a Markov process computation solves the community structure problem effectively. | LitMetric

Potts model based on a Markov process computation solves the community structure problem effectively.

Phys Rev E Stat Nonlin Soft Matter Phys

Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing 100190, China.

Published: July 2012

AI Article Synopsis

Article Abstract

The Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of the Potts model. Specifically we model the community structure detection Potts procedure by a Markov process, which has a clear mathematical explanation. Then we show that the local uniform behavior of spin values across multiple timescales in the representation of the Markov variables could naturally reveal the network's hierarchical community structure. In addition, critical topological information regarding multivariate spin configuration could also be inferred from the spectral signatures of the Markov process. Finally an algorithm is developed to determine fuzzy communities based on the optimal number of communities and the stability across multiple timescales. The effectiveness and efficiency of our algorithm are theoretically analyzed as well as experimentally validated.

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Source
http://dx.doi.org/10.1103/PhysRevE.86.016109DOI Listing

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