Online Inference of Mixed Membership Stochastic Blockmodels for Network Data Streams

• KOBAYASHI Tomoki

  Graduate School of System Informatics, Kobe University

• EGUCHI Koji

  Graduate School of System Informatics, Kobe University

Abstract

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E97.D (4), 752-761, 2014

  The Institute of Electronics, Information and Communication Engineers

Keywords

• mixed membership stochastic blockmodels
• particle filters
• dynamic networks
• online inference

Details

• CRID: 1390001204377778688
• NII Article ID: 130003394900
• DOI: 10.1587/transinf.e97.d.752
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E97.D/4/E97.D_752/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed