A novel dynamics combination model reveals the hidden information of community structure

Abstract

The analysis of the dynamic details of community structure is an important question for scientists from many fields. In this paper, we propose a novel Markov–Potts framework to uncover the optimal community structures and their stabilities across multiple timescales. Specifically, we model the Potts dynamics to detect community structure by a Markov process, which has a clear mathematical explanation. Then the local uniform behavior of spin values revealed by our model is shown that can naturally reveal the stability of hierarchical community structure across multiple timescales. To prove the validity, phase transition of stochastic dynamic system is used to indicate that the stability of community structure we proposed is able to describe the significance of community structure based on eigengap theory. Finally, we test our framework on some example networks and find it does not have resolute limitation problem at all. Results have shown the model we proposed is able to uncover hierarchical structure in different scales effectively and efficiently.