research-article

Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization

Authors:

Quan WangAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 31, Issue 2

Pages 273 - 286

https://doi.org/10.1109/TKDE.2018.2832205

Published: 01 February 2019 Publication History

Abstract

Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel $K$ -means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a S emi- S upervised j oint N onnegative M atrix F actorization algorithm ( S2-jNMF ) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the S2-jNMF algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms the state-of-the-art approaches for community detection in multi-layer networks.

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 31, Issue 2

February 2019

200 pages

ISSN:1041-4347

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Copyright © 2019.

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IEEE Educational Activities Department

United States

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Published: 01 February 2019

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Cited By

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Wu DWang PLiang JLu JXu JWang RNie F(2024)Adaptive Local Modularity Learning for Efficient Multilayer Graph ClusteringIEEE Transactions on Signal Processing10.1109/TSP.2024.338565472(2221-2232)Online publication date: 10-Apr-2024
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