Flexible and robust multi-network clustering

Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

Integrating multiple graphs (or networks) has been shown to be a promising approach to improve the graph clustering accuracy. Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In these methods, a network is treated as a view or domain. The key assumption is that there is a common clustering structure shared across all views (domains), and different views (domains) provide compatible and complementary information on this underlying clustering structure. However, in many emerging real-life applications, different networks have different data distributions, where the assumption that all networks share a single common clustering structure does not hold. In this paper, we propose a flexible and robust framework that allows multiple underlying clustering structures across different networks. Our method models the domain similarity as a network, which can be utilized to regularize the clustering structures in different networks. We refer to such a data model as a network of networks (NoN). We develop NoNClus, a novel method based on non-negative matrix factorization (NMF), to cluster an NoN. We provide rigorous theoretical analysis of NoNClus in terms of its correctness, convergence and complexity. Extensive experimental results on synthetic and real-life datasets show the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationKDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages835-844
Number of pages10
ISBN (Electronic)9781450336642
DOIs
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2015-August

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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  • Cite this

    Ni, J., Tong, H., Fan, W., & Zhang, X. (2015). Flexible and robust multi-network clustering. In KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 835-844). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2015-August). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783262