25 Citations (Scopus)

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 publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages835-844
Number of pages10
Volume2015-August
ISBN (Print)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

Other

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

Fingerprint

Factorization
Data structures

Keywords

  • Graph clustering
  • Network of networks

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

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

Flexible and robust multi-network clustering. / Ni, Jingchao; Tong, Hanghang; Fan, Wei; Zhang, Xiang.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. p. 835-844.

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

Ni, J, Tong, H, Fan, W & Zhang, X 2015, Flexible and robust multi-network clustering. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 2015-August, Association for Computing Machinery, pp. 835-844, 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015, Sydney, Australia, 8/10/15. https://doi.org/10.1145/2783258.2783262
Ni J, Tong H, Fan W, Zhang X. Flexible and robust multi-network clustering. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August. Association for Computing Machinery. 2015. p. 835-844 https://doi.org/10.1145/2783258.2783262
Ni, Jingchao ; Tong, Hanghang ; Fan, Wei ; Zhang, Xiang. / Flexible and robust multi-network clustering. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. pp. 835-844
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