Title of article :
A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks
Author/Authors :
Wang, Guo-Zheng School of Management - Shanghai University, China , Xiong, Li School of Management - Shanghai University, China , Liu, Hu-Chen School of Management - Shanghai University, China
Pages :
12
From page :
1
To page :
12
Abstract :
Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.
Keywords :
Bipartite Networks , Number of Communities , Estimating , Monte Carlo Sampling , Bayesian Inference Method
Journal title :
Scientific Programming
Serial Year :
2019
Full Text URL :
Record number :
2611148
Link To Document :
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