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| Quantum-Behaved Discrete Particle Swarm Optimization for Complex Network Clustering |
| YANG Zhongbao1,2, CHU Yangjie2, HONG Ye2, JIANG Dengying2
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1.School of Mathematics and Statistic, Qiannan Normal University for Natinalities, Douyun 558000, China;
2.Wuhan University of Technology,Wuhan 430070,China |
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Abstract Community structure is one of the most important features of complex network. In order to solve the problem of resolution limit of modularity optimization methods, a quantum-behaved discrete particle swarm optimization for complex network clustering is proposed in non-overlapping community detection algorithm (NQD-PSO). The core node and neighborly common nodes are constructed asa motif, which is the initial value of the quantum particle swarm optimization algorithm. At the same time, constructing the motif weighted community clustering function as the adaptive function of the algorithm, while it can use the triangular model to judge the problem of community stability measurement for quantifying the stability of community then the compression factor is adopted to adjust the global and local search model, which makes the algorithm globally converge by combining with quantum particle swarm optimization. Compared with other algorithms, NQD-PSO algorithm uses motif orderly table coding method, and experimental results on both synthetic and real datasets show thatthe NQD-PSO algorithm can mine more high-quality community structures.
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Received: 24 February 2018
Published: 04 July 2019
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