The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF

Xiaochun Cao; Xiao Wang; Di Jin; Yixin Cao; Dongxiao He

doi:10.1145/2567948.2577307

The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF

Xiaochun Cao, Xiao Wang, Di Jin, Yixin Cao, Dongxiao He

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

5 Citations (Scopus)

Abstract

The detection of communities in various networks has been considered by many researchers. Moreover, it is preferable for a community detection method to detect hubs and out- liers as well. This becomes even more interesting and chal- lenging when taking the unsupervised assumption, that is, we do not assume the prior knowledge of the number K of communities. In this poster, we define a novel model to identify overlapping communities as well as hubs and out- liers. When K is given, we propose a normalized symmetric nonnegative matrix factorization algorithm to learn the pa- rameters of the model. Otherwise, we introduce a Bayesian symmetric nonnegative matrix factorization to learn the pa- rameters of the model, while determining K.Our experiment indicates its superior performance on various networks.

Original language	English
Title of host publication	WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
Publisher	Association for Computing Machinery, Inc
Pages	233-234
Number of pages	2
ISBN (Electronic)	9781450327459
DOIs	https://doi.org/10.1145/2567948.2577307
Publication status	Published - 7 Apr 2014
Externally published	Yes
Event	23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of Duration: 7 Apr 2014 → 11 Apr 2014

Conference

Conference	23rd International Conference on World Wide Web, WWW 2014
Country/Territory	Korea, Republic of
City	Seoul
Period	7/04/14 → 11/04/14

Keywords

(Bayesian) NMF
Community
Hubs
Outliers

ASJC Scopus subject areas

Computer Networks and Communications
Software

More information

10.1145/2567948.2577307

Cite this

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title = "The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF",

abstract = "The detection of communities in various networks has been considered by many researchers. Moreover, it is preferable for a community detection method to detect hubs and out- liers as well. This becomes even more interesting and chal- lenging when taking the unsupervised assumption, that is, we do not assume the prior knowledge of the number K of communities. In this poster, we define a novel model to identify overlapping communities as well as hubs and out- liers. When K is given, we propose a normalized symmetric nonnegative matrix factorization algorithm to learn the pa- rameters of the model. Otherwise, we introduce a Bayesian symmetric nonnegative matrix factorization to learn the pa- rameters of the model, while determining K.Our experiment indicates its superior performance on various networks.",

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author = "Xiaochun Cao and Xiao Wang and Di Jin and Yixin Cao and Dongxiao He",

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Cao, X, Wang, X, Jin, D, Cao, Y & He, D 2014, The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF. in WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc, pp. 233-234, 23rd International Conference on World Wide Web, WWW 2014, Seoul, Korea, Republic of, 7/04/14. https://doi.org/10.1145/2567948.2577307

The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF. / Cao, Xiaochun; Wang, Xiao; Jin, Di et al.
WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc, 2014. p. 233-234.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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