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Two-stage multi-objective evolutionary algorithm for overlapping community discovery
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DOI：10.7717/peerj-cs.2185.png)
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Abstract
En 中文
As one of the essential topological structures in complex networks, community structure has signi fi cant theoretical and application value and has attracted the attention of researchers in many fi elds. In a social network, individuals may belong to different communities simultaneously, such as a workgroup and a hobby group. Therefore, overlapping community discovery can help us understand and model the network structure of these multiple relationships more accurately. This article proposes a two-stage multi-objective evolutionary algorithm for overlapping community discovery problem. First, using the initialization method to divide the central node based on node degree, combined with the cross-mutation evolution strategy of the genome matrix, the fi rst stage of non-overlapping community division is completed on the decomposition-based multi-objective optimization framework. Then, based on the result set of the fi rst stage, appropriate nodes are selected from each individual ' s community as the central node of the initial population in the second stage, and the fuzzy threshold is optimized through the fuzzy clustering method based on evolutionary calculation and the feedback model, to fi nd reasonable overlapping nodes. Finally, tests are conducted on synthetic datasets and real datasets. The statistical results demonstrate that compared with other representative algorithms, this algorithm performs optimally on test instances and has better results.
Keywords:
Overlapping community discovery
Evolutionary clustering
Fuzzy clustering
Feedback model
Algorithm design
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