Proximity Preserving Nonnegative Matrix Factorization

• Ogawa Yuya

  Graduate School of Information Science of Technology, Osaka University

• Takeuchi Koh

  Graduate School of Informatics, Kyoto University

• Sasaki Yuya

  Graduate School of Information Science of Technology, Osaka University

• Onizuka Makoto

  Graduate School of Information Science of Technology, Osaka University

Abstract

We consider the problem of community detection. Although network embedding and representation learning methods are recently getting popular, we claim that they fall into suboptimal solutions for community detection, because they are based on indirect approach, which requires to apply clustering methods such as k-means to the embedding/representation vectors. We present PPNMF, proximity preserving nonnegative matrix factorization for community detection. The idea of PPNMF is three-fold. 1) PPNMF is based on direct approach: it directly minimizes its loss function for community detection. 2) Users can control the importance of observed edges over unobserved edges. 3) PPNMF can precisely capture the effects of the first-order and second-order proximities of vertexes to communities. Also, PPNMF employs the Adamic Adar index as the second-order proximity. The experiments validate that PPNMF performs better or comparable to existing methods in various real datasets for the tasks of community detection.

Journal

• Journal of Information Processing

  Journal of Information Processing 28 (0), 445-452, 2020

  Information Processing Society of Japan

Keywords

• community detection
• NMF
• Network analysis

Details

• CRID: 1390003825205790976
• NII Article ID: 130007887839
• DOI: 10.2197/ipsjjip.28.445
• ISSN: 18826652
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/28/0/28_445/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed