New Attributed Graph Clustering by Bridging Attribute and Topology Spaces

• Maekawa Seiji

  Graduate School of Information Science and Technology, Osaka University

• Takeuchi Koh

  Graduate School of informatics, Kyoto University

• Onizuka Makoto

  Graduate School of Information Science and Technology, Osaka University

Abstract

We consider the clustering problem of attributed graphs. Our challenge is how we design an effective clustering method that captures the complicated relationship between the topology and the attributes in real-world graphs. We propose NAGC, a new attributed graph clustering method that bridges the attribute space and the topology space. The feature of NAGC is two-hold. 1) NAGC learns a projection function between the topology space and the attribute space so as to capture their complicated relationship, and 2) NAGC leverages the positive unlabeled learning to take the effect of partially observed positive edges into the cluster assignment. We conducted experiments extensively to validate that NAGC performs higher than or comparable to prior arts regarding the clustering quality.

Journal

• Journal of Information Processing

  Journal of Information Processing 28 (0), 427-435, 2020

  Information Processing Society of Japan

Keywords

• attributed graph clustering
• NMF
• PU learning

Details

• CRID: 1390285300182492288
• NII Article ID: 130007887721
• DOI: 10.2197/ipsjjip.28.427
• ISSN: 18826652
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/28/0/28_427/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed