Fusion and community detection in multi-layer graphs

Gligorijević, Vladimir, Panagakis, Yannis ORCID logoORCID: https://orcid.org/0000-0003-0153-5210 and Zafeiriou, Stefanos (2016) Fusion and community detection in multi-layer graphs. 2016 23rd International Conference on Pattern Recognition (ICPR). In: 2016 23rd International Conference on Pattern Recognition (ICPR), 04-08 Dec 2016, Cancun, Mexico. ISBN 9781509048472. [Conference or Workshop Item] (doi:10.1109/ICPR.2016.7899821)

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Relational data arising in many domains can be represented by networks (or graphs) with nodes capturing entities and edges representing relationships between these entities. Community detection in networks has become one of the most important problems having a broad range of applications. Until recently, the vast majority of papers have focused on discovering community structures in a single network. However, with the emergence of multi-view network data in many real-world applications and consequently with the advent of multilayer graph representation, community detection in multi-layer graphs has become a new challenge. Multi-layer graphs provide complementary views of connectivity patterns of the same set of vertices. Fusion of the network layers is expected to achieve better clustering performance. In this paper, we propose two novel methods, coined as WSSNMTF (Weighted Simultaneous Symmetric Non-Negative Matrix Tri-Factorization) and NG-WSSNMTF (Natural Gradient WSSNMTF), for fusion and clustering of multi-layer graphs. Both methods are robust with respect to missing edges and noise. We compare the performance of the proposed methods with two baseline methods, as well as with three state-of-the-art methods on synthetic and three real-world datasets. The experimental results indicate superior performance of the proposed methods.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23778
Notes on copyright: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
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Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 18:46
Last Modified: 29 Nov 2022 21:25
URI: https://eprints.mdx.ac.uk/id/eprint/23778

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