ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (8): 1650-1662.doi: 10.7544/issn1000-1239.2020.20200158

Special Issue: 2020数据挖掘与知识发现专题

Previous Articles     Next Articles

A Degree Corrected Stochastic Block Model for Attributed Networks

Zheng Yimei1,2, Jia Caiyan1,2, Chang Zhenhai3, Li Xuanya4   

  1. 1(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044);2(Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University), Beijing 100044);3(School of Mathematics and Statistics, Tianshui Normal University, Tianshui, Gansu 741000);4(Baidu Online Network Technology (Beijing) Co., Ltd, Beijing 100085)
  • Online:2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61876016, 61632004), the Fundamental Research Funds for the Central Universities (2019JBZ110), and the Baidu Pinecone Program.

Abstract: Community detection is an important task in complex network analysis. The existing community detection methods mostly focus on utilizing the simple network structure, while the methods of integrating network topology and node attributes are also mainly aimed at the traditional community structure, which fails to detect the bipartite structure, mixed structure, etc. However, the degree of each node in the network will affect the composition of the links in the network, as well as the distribution of the community structure. This paper proposes a method called DPSB_PG for attributed networks community detection based on the stochastic block model. Unlike other generative models for attributed networks, in this method, the generation of node links and node attributes both followes the Poisson distribution, and considers the probability between communities based on the stochastic block model. Moreover, the idea of degree corrected is integrated in the process of generating node links. Finally, in order to obtain the community membership of nodes, the expectation-maximization algorithm is used to infer the parameters of the model. The experimental results on the real networks show that the DPSB_PG inherits the advantages of the stochastic block model and can detect the general community structure in networks. Since the introduction of the idea of degree corrected, this model has a good data fitting ability. Overall, the performance of this model is superior to other existing state-of-the-art community detection algorithms for both attributed networks and non-attributed networks.

Key words: degree corrected, Poisson distribution, stochastic block model, general structure, attributed networks

CLC Number: