Egonet tensor decomposition for community identification

Fatemeh Sheikholeslami, Brian Baingana, Georgios B. Giannakis, Nikolaos D. Sidiropoulos

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

4 Scopus citations

Abstract

Real-world networks are known to exhibit community structure, characterized by presence of dense node clusters with loose edge connections among them. Although identification of communities is a well-studied subject, most approaches only focus on edge-based criteria which may not incorporate important grouping information captured by higher-order structures e.g., cliques and cycles, to name a few. In order to overcome this limitation, the present paper advocates a novel three-way tensor network representation that captures spatial dependencies among node neighborhoods. Each tensor slice captures a connectivity matrix pertaining to a unique egonet, defined as the subgraph induced by a node and its single-hop neighbors. Constrained tensor factorization is pursued to reveal the hidden and possibly overlapping community structure. Numerical tests on synthetic and real world networks corroborate the efficacy of the novel approach.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages341-345
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • CPD
  • Community detection
  • Egonets
  • PARAFAC
  • Social networks
  • Tensor decomposition

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    Sheikholeslami, F., Baingana, B., Giannakis, G. B., & Sidiropoulos, N. D. (2017). Egonet tensor decomposition for community identification. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 341-345). [7905860] (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7905860