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 language | English (US) |
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Title of host publication | Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 |
Editors | Michael B. Matthews |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 980-984 |
Number of pages | 5 |
ISBN (Electronic) | 9781538618233 |
DOIs | |
State | Published - Jul 2 2017 |
Event | 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States Duration: Oct 29 2017 → Nov 1 2017 |
Publication series
Name | Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 |
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Volume | 2017-October |
Other
Other | 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 10/29/17 → 11/1/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Overlapping community detection
- egonet subgraphs
- tensor decomposition