A new nature-inspired optimization for community discovery in complex networks

Xiaoyu Li, Chao Gao, Songxin Wang, Zhen Wang, Chen Liu and Xianghua Li ()
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Xiaoyu Li: Northwestern Polytechnical University
Chao Gao: Northwestern Polytechnical University
Songxin Wang: Shanghai University of Finance and Economics
Zhen Wang: Northwestern Polytechnical University
Chen Liu: Northwestern Polytechnical University
Xianghua Li: Northwestern Polytechnical University

The European Physical Journal B: Condensed Matter and Complex Systems, 2021, vol. 94, issue 7, 1-14

Abstract: Abstract The community structure, owing to its significant status, is of extraordinary significance in comprehending and detecting inherent functions in real networks. However, the community structures are always hard to be identified, and whether the existing algorithms are based on optimization or heuristics, the robustness and accuracy should be improved. The physarum (i.e., slime molds with multi heads) has proved its ability to produce foraging networks. Therefore, we adopt physarum so that the optimization-based community detection algorithms can work more efficiently. Specifically, a physarum-based network model (pnm), which is capable of identifying inter-edges of the community in a network, is used to optimize the prior knowledge of existing evolutional algorithms (i.e., genetic algorithm, particle swarm optimization algorithm and ant colony algorithm). the optimized algorithms have been compared with some advanced methods in synthetic and real networks. experimental results have verified the effectiveness of the proposed method. Graphic abstract

Date: 2021
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DOI: 10.1140/epjb/s10051-021-00122-x

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