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 proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages233-234
Number of pages2
ISBN (Electronic)9781450327459
DOIs
Publication statusPublished - 7 Apr 2014
Externally publishedYes
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 7 Apr 201411 Apr 2014

Conference

Conference23rd International Conference on World Wide Web, WWW 2014
Country/TerritoryKorea, Republic of
CitySeoul
Period7/04/1411/04/14

Keywords

  • (Bayesian) NMF
  • Community
  • Hubs
  • Outliers

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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