Self-grouping multi-network clustering

Jingchao Ni, Wei Cheng, Wei Fan, Xiang Zhang

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

Abstract

Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multidomain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, COMCLUS, to simultaneously group and cluster multiple networks. COMCLUS treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1119-1124
Number of pages6
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jan 31 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

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

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
CountrySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Ni, J., Cheng, W., Fan, W., & Zhang, X. (2017). Self-grouping multi-network clustering. In F. Bonchi, X. Wu, R. Baeza-Yates, J. Domingo-Ferrer, & Z-H. Zhou (Eds.), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016 (pp. 1119-1124). [7837959] (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2016.10
Ni, Jingchao ; Cheng, Wei ; Fan, Wei ; Zhang, Xiang. / Self-grouping multi-network clustering. Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. editor / Francesco Bonchi ; Xindong Wu ; Ricardo Baeza-Yates ; Josep Domingo-Ferrer ; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1119-1124 (Proceedings - IEEE International Conference on Data Mining, ICDM).
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title = "Self-grouping multi-network clustering",
abstract = "Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multidomain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, COMCLUS, to simultaneously group and cluster multiple networks. COMCLUS treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.",
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Ni, J, Cheng, W, Fan, W & Zhang, X 2017, Self-grouping multi-network clustering. in F Bonchi, X Wu, R Baeza-Yates, J Domingo-Ferrer & Z-H Zhou (eds), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016., 7837959, Proceedings - IEEE International Conference on Data Mining, ICDM, Institute of Electrical and Electronics Engineers Inc., pp. 1119-1124, 16th IEEE International Conference on Data Mining, ICDM 2016, Barcelona, Catalonia, Spain, 12/12/16. https://doi.org/10.1109/ICDM.2016.10

Self-grouping multi-network clustering. / Ni, Jingchao; Cheng, Wei; Fan, Wei; Zhang, Xiang.

Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. ed. / Francesco Bonchi; Xindong Wu; Ricardo Baeza-Yates; Josep Domingo-Ferrer; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1119-1124 7837959 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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

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AB - Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multidomain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, COMCLUS, to simultaneously group and cluster multiple networks. COMCLUS treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.

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Ni J, Cheng W, Fan W, Zhang X. Self-grouping multi-network clustering. In Bonchi F, Wu X, Baeza-Yates R, Domingo-Ferrer J, Zhou Z-H, editors, Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1119-1124. 7837959. (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2016.10