research-article

Community Detection in Multiplex Networks Based on Evolutionary Multitask Optimization and Evolutionary Clustering Ensemble

Authors:

Chin-Teng LinAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 27, Issue 3

Pages 728 - 742

https://doi.org/10.1109/TEVC.2022.3184988

Published: 01 June 2023 Publication History

Abstract

Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this article, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm that can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. First, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multitask optimization (MTO) problem and employ an evolutionary MTO algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Second, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.

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Cited By

Zhou PHu BYan DDu L(2024)Clustering Ensemble via Diffusion on Adaptive MultiplexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331140936:4(1463-1474)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3311409
Gao FGao WHuang LZhang SGong MWang L(2024)Effective transferred knowledge identified by bipartite graph for multiobjective multitasking optimizationKnowledge-Based Systems10.1016/j.knosys.2024.111530290:COnline publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111530

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 27, Issue 3

June 2023

345 pages

ISSN:1089-778X

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1089-778X © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 June 2023

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Cited By

Zhou PHu BYan DDu L(2024)Clustering Ensemble via Diffusion on Adaptive MultiplexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331140936:4(1463-1474)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3311409
Gao FGao WHuang LZhang SGong MWang L(2024)Effective transferred knowledge identified by bipartite graph for multiobjective multitasking optimizationKnowledge-Based Systems10.1016/j.knosys.2024.111530290:COnline publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111530

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