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 - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages291-300
Number of pages10
ISBN (Electronic)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

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

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

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Experiments

All Science Journal Classification (ASJC) codes

  • 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 C. Aggarwal, Z-H. Zhou, A. Tuzhilin, H. Xiong, & X. Wu (Eds.), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 (pp. 291-300). [7373333] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2016-January). Institute of Electrical and Electronics Engineers Inc.. 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 - 15th IEEE International Conference on Data Mining, ICDM 2015. editor / Charu Aggarwal ; Zhi-Hua Zhou ; Alexander Tuzhilin ; Hui Xiong ; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 291-300 (Proceedings - IEEE International Conference on Data Mining, ICDM).
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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.",
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Liu, R, Cheng, W, Tong, H, Wang, W & Zhang, X 2016, Robust multi-network clustering via joint cross-domain cluster alignment. in C Aggarwal, Z-H Zhou, A Tuzhilin, H Xiong & X Wu (eds), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015., 7373333, Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2016-January, 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

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

Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. ed. / Charu Aggarwal; Zhi-Hua Zhou; Alexander Tuzhilin; Hui Xiong; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 291-300 7373333 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2016-January).

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

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Liu R, Cheng W, Tong H, Wang W, Zhang X. Robust multi-network clustering via joint cross-domain cluster alignment. In Aggarwal C, Zhou Z-H, Tuzhilin A, Xiong H, Wu X, editors, Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 291-300. 7373333. (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2015.13