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A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks
Joint Authors
Xiong, Li
Liu, Hu-Chen
Wang, Guo-Zheng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Guo-Zheng& Xiong, Li& Liu, Hu-Chen. 2019. A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks. Scientific Programming،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1210774
Modern Language Association (MLA)
Wang, Guo-Zheng…[et al.]. A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks. Scientific Programming No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1210774
American Medical Association (AMA)
Wang, Guo-Zheng& Xiong, Li& Liu, Hu-Chen. A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1210774
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210774