Robust multi-network clustering via joint cross-domain cluster alignment

Rui Liu, Wei Cheng, Hanghang Tong, Wei Wang, Xiang Zhang

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

8 Citations (Scopus)

Abstract

Network clustering is an important problem thathas recently drawn a lot of attentions. Most existing workfocuses on clustering nodes within a single network. In manyapplications, however, there exist multiple related networks, inwhich each network may be constructed from a different domainand instances in one domain may be related to instances in otherdomains. In this paper, we propose a robust algorithm, MCA, formulti-network clustering that takes into account cross-domain relationshipsbetween instances. MCA has several advantages overthe existing single network clustering methods. First, it is ableto detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networksby leveraging the duality between clustering individual networksand inferring cross-network cluster alignment. Finally, it providesa multi-network clustering solution that is more robust to noiseand errors. We perform extensive experiments on a variety ofreal and synthetic networks to demonstrate the effectiveness andefficiency of MCA.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages291-300
Number of pages10
Volume2016-January
ISBN (Print)9781467395038
DOIs
StatePublished - Jan 5 2016
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

Fingerprint

Experiments

Keywords

  • Graph Clustering
  • Multi-network

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Liu, R., Cheng, W., Tong, H., Wang, W., & Zhang, X. (2016). Robust multi-network clustering via joint cross-domain cluster alignment. In Proceedings - IEEE International Conference on Data Mining, ICDM (Vol. 2016-January, pp. 291-300). [7373333] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2015.13

Robust multi-network clustering via joint cross-domain cluster alignment. / Liu, Rui; Cheng, Wei; Tong, Hanghang; Wang, Wei; Zhang, Xiang.

Proceedings - IEEE International Conference on Data Mining, ICDM. Vol. 2016-January Institute of Electrical and Electronics Engineers Inc., 2016. p. 291-300 7373333.

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

Liu, R, Cheng, W, Tong, H, Wang, W & Zhang, X 2016, Robust multi-network clustering via joint cross-domain cluster alignment. in Proceedings - IEEE International Conference on Data Mining, ICDM. vol. 2016-January, 7373333, Institute of Electrical and Electronics Engineers Inc., pp. 291-300, 15th IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, United States, 11/14/15. https://doi.org/10.1109/ICDM.2015.13
Liu R, Cheng W, Tong H, Wang W, Zhang X. Robust multi-network clustering via joint cross-domain cluster alignment. In Proceedings - IEEE International Conference on Data Mining, ICDM. Vol. 2016-January. Institute of Electrical and Electronics Engineers Inc. 2016. p. 291-300. 7373333 https://doi.org/10.1109/ICDM.2015.13
Liu, Rui ; Cheng, Wei ; Tong, Hanghang ; Wang, Wei ; Zhang, Xiang. / Robust multi-network clustering via joint cross-domain cluster alignment. Proceedings - IEEE International Conference on Data Mining, ICDM. Vol. 2016-January Institute of Electrical and Electronics Engineers Inc., 2016. pp. 291-300
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