4.7 Article

A generative model for exploring structure regularities in attributed networks

Journal

INFORMATION SCIENCES
Volume 505, Issue -, Pages 252-264

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.07.084

Keywords

Attributed network; Community detection; General structure; Stochastic blockmodel; EM algorithm

Funding

  1. National Science Foundation of China [61876016, 61632004]
  2. Beijing Municipal Science and Technology Commission [Z181100008918012]

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Many real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. In this paper, by characterizing the potential relationship between communities of links and node attributes, a principled statistical model named PSB_PG that generates link topology and node attributes is proposed. This model for generating links is based on the stochastic blockmodels following a Poisson distribution. Therefore, it is capable of detecting a wide range of network structures including community structures, bipartite structures, and other mixture structures. The model for generating node attributes assumes that node attributes are high-dimensional, sparse, and also follow a Poisson distribution. This makes the model be uniform, and the model parameters can be directly estimated by the expectation-maximization (EM) algorithm. Experimental results on artificial networks and real networks containing various structures have shown that the proposed model PSB_PG is not only competitive with the state-of-the-art models, but also provides a good semantic interpretation for each community via the learned relationship between the community and its related attributes. (C) 2019 Elsevier Inc. All rights reserved.

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