Community detection in social network with pairwisely constrained symmetric non-negative Matrix Factorization

Xiaohua Shi, Hongtao Lu, Yangchen He, Shan He

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

29 Citations (Scopus)

Abstract

Non-negative Matrix Factorization (NMF) aims to find two non-negative matrices whose product approximates the original matrix well, and is widely used in clustering condition with good physical interpretability and universal applicability. Detecting communities with NMF can keep non-negative network physical definition and effectively capture communities-based structure in the low dimensional data space. However some NMF methods in community detection did not concern with more network inner structures or existing ground-truth community information. In this paper, we propose a novel pairwisely constrained nonnegative symmetric matrix factorization (PCSNMF) method, which not only consider symmetric community structures of undirected network, but also takes into consideration the pairwise constraints generated from some ground-truth group information to enhance the community detection. We compare our approaches with other NMF-based methods in three social networks, and experimental results for community detection show that our approaches are all feasible and achieve better community detection results.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
EditorsJian Pei, Jie Tang, Fabrizio Silvestri
PublisherAssociation for Computing Machinery
Pages541-546
Number of pages6
ISBN (Electronic)9781450338547
DOIs
Publication statusPublished - 25 Aug 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: 25 Aug 201528 Aug 2015

Publication series

NameProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Conference

ConferenceIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Country/TerritoryFrance
CityParis
Period25/08/1528/08/15

Bibliographical note

Funding Information:
A. Acknowledgments This work was supported by NSFC (no. 61272247 and 60873133), the Science and Technology Commission of Shanghai Municipality (Grant No. 13511500200), 863(No.2008AA02Z310) in China, the Arts and Science Cross Special Fund of Shanghai Jiao Tong University under Grant 13JCY14.

Publisher Copyright:
© 2015 ACM.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Community detection
  • Non-negative Matrix Factorization
  • Pairwise constraints
  • Semi-supervised learning
  • Symmetric matrix

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

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