Joint role and community detection in networks via L2,1 norm regularized nonnegative matrix tri-factorization

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Samenvatting

Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.

Originele taal-2Engels
TitelProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
RedacteurenFrancesca Spezzano, Wei Chen, Xiaokui Xiao
UitgeverijAssociation for Computing Machinery, Inc
Pagina's168-175
Aantal pagina's8
ISBN van elektronische versie9781450368681
DOI's
StatusGepubliceerd - 27 aug 2019
Evenement11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duur: 27 aug 201930 aug 2019

Congres

Congres11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
LandCanada
StadVancouver
Periode27/08/1930/08/19

Vingerafdruk Duik in de onderzoeksthema's van 'Joint role and community detection in networks via L<sub>2,1</sub> norm regularized nonnegative matrix tri-factorization'. Samen vormen ze een unieke vingerafdruk.

  • Citeer dit

    Pei, Y., Fletcher, G., & Pechenizkiy, M. (2019). Joint role and community detection in networks via L2,1 norm regularized nonnegative matrix tri-factorization. In F. Spezzano, W. Chen, & X. Xiao (editors), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (blz. 168-175). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341161.3342886