Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Evolutionary Computation, 2022, PP, (99), pp. 1-1
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble.pdf | Published version | 2.31 MB |
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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 paper, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm which can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. Firstly, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multi-task optimization problem and employ an evolutionary multi-task optimization algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Secondly, 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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