Quantum inspired evolutionary algorithm for community detection in complex networks |
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Authors: | Meng Yuanyuan Liu Xiyu |
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Affiliation: | 1. School of Management Science and Engineering, Shandong Normal University, China;2. School of Computer Science and Technology, Shandong University of Finance and Economics, China |
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Abstract: | Community structure is indispensable to discover the potential property of complex network systems. In this paper we propose two algorithms (QIEA-net and iQIEA-net) to discover communities in social networks by optimizing modularity. Unlike many existing methods, the proposed algorithms adopt quantum inspired evolutionary algorithm (QIEA) to optimize a population of solutions and do not need to give the number of community beforehand, which is determined by optimizing the value of modularity function and needs no human intervention. In order to accelerate the convergence speed, in iQIEA-net, we apply the result of classical partitioning algorithm as a guiding quantum individual, which can instruct other quantum individuals' evolution. We demonstrate the potential of two algorithms on five real social networks. The results of comparison with other community detection algorithms prove our approaches have very competitive performance. |
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Keywords: | Quantum Complex networks Community detection |
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