https://doi.org/10.1140/epjb/s10051-021-00122-x
Regular Article - Statistical and Nonlinear Physics
A new nature-inspired optimization for community discovery in complex networks
1
School of Cybersecurity, Northwestern Polytechnical University, 710072, Xi’an, Shaanxi, China
2
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, 710072, Xi’an, Shaanxi, China
3
School of Information Management and Engineering, Shanghai University of Finance and Economics, 200433, Shanghai, China
4
School of Ecology and Environment, Northwestern Polytechnical University, 710072, Xi’an, Shaanxi, China
Received:
28
March
2021
Accepted:
12
May
2021
Published online:
7
July
2021
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.
© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2021
