Soft unveiling of communities via egonet tensors

Fatemeh Sheikholeslami, Georgios B. Giannakis

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

Abstract

The task of community detection over a network pertains to identifying the underlying groups of nodes whose often-hidden association has manifested itself in dense connections among the members, and sparse inter-community links. The present work aims at improving the robustness of the traditional matrix-based community detection algorithms via capturing multi-hop connectivity patterns through tensor analysis. To this end, a novel tensor-based network representation is advocated in this contribution, and the task of community detection is cast as a constrained PARAFAC decomposition. Subsequently, the proposed tri-linear minimization is handled via alternating least-squares, where intermediate subproblems are solved using the alternating direction method of multipliers (ADMM) to ensure convergence. The framework is further broadened to accommodate time-varying graphs, where the edgeset as well as the underlying communities evolve through time. Numerical tests corroborate the increased robustness provided through the novel representation as well as the proposed tensor decomposition.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages980-984
Number of pages5
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Publication series

NameConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume2017-October

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/TerritoryUnited States
CityPacific Grove
Period10/29/1711/1/17

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grants 1500713, 1442686, 1514056, and NIH grant no. 1R01GM104975-01.

Funding Information:
1Work in this paper was supported by NSF grants 1500713, 1442686, 1514056, and NIH grant no. 1R01GM104975-01.

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Overlapping community detection
  • egonet subgraphs
  • tensor decomposition

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