Abstract
Estimation of Distribution Algorithm (EDA) is a stochastic optimization algorithm based on statistical theory. It has strong global search ability, but it is easy to fall into the local optimal solution and can not get good results in community detection. In order to solve this problem, we propose a community detection algorithm based on Estimation of Distribution Algorithm, named EDACD, whose basic framework refers EDA and the target function is modularity. EDACD keeps population diversity by adding crossover mutation operation of Genetic Algorithm as well as the improvement of probability model. Genetic Algorithm is based on “micro” level of gene, which has good local optimization ability; EDA uses the evolutionary method based on “macro” level of search space, which has strong global search ability and fast convergence speed. Taking advantage of the two methods, EDACD can used to improve the search ability of algorithm from “micro” and “macro” two levels. Finally, by experimenting on some typical real-world networks and computer-generated networks, the experimental results show that the proposed algorithm can detect the community division accurately, and has higher clustering precision compared with some representative algorithms. In addition, the proposed algorithm also has a fast convergence rate.
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References
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Adamic, L.A., et al.: Power-law distribution of the world wide web. Science 287(5461), 2115 (2000)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002)
Yang, B., et al.: Complex network clustering algorithms. J. Softw. 20(1), 54–66 (2009)
Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38(2), 321–330 (2004)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 69(6 Pt 2), 066133 (2004)
Guimerà, R., Amaral, L.A.N.: Functional cartography of complex metabolic networks. Nature 433(7028), 895–900 (2005)
Yang, B., Cheung, W., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 76(3 Pt 2), 036106 (2007)
Jin, D., et al.: k-Nearest-neighbor network based data clustering algorithm. Pattern Recog. Artif. Intell. 23(4), 546–551 (2010)
Zhang, X., et al.: Label propagation algorithm based on local cycles for community detection. Int. J. Mod. Phys. B 29(5), 1550029 (2015)
Peng, H., et al.: An improved label propagation algorithm using average node energy in complex networks. Phys. A 460, 98–104 (2016)
Liu, H.: Genetic algorithm optimizing modularity for community detection in complex networks. In: Proceedings of the 35th Chinese Control Conference (CCC), pp. 1252–1256. IEEE (2016)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms, vol. 64, no. 5, pp. 1140–1148. Springer, Boston (2002)
Liu, G.S., Zhang, H.L., Meng, K., et al.: Non-random label propagation community detection algorithm. J. Shanghai Jiao Tong Univ. 49(8), 1168–1173 (2015)
Izquierdo, C.E., Velarde, J.L.G., Batista, B.M., Moreno-Vega, J.M.: Estimation of distribution algorithm for the quay crane scheduling problem. In: Pelta, D.A., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds.) Nature Inspired Cooperative Strategies for Optimization, NICSO 2011, vol. 387, pp. 4063–4076. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24094-2_13
Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 80(1), 016118 (2009)
Danon, L., et al.: Comparing community structure identification. J. Stat. Mech. Theory Exp. 2005(9), 09008 (2005)
Liu, D., Jin, D., He, D., Huang, J., Yang, J., Yang, B.: Community mining in complex networks. J. Comput. Res. Dev. 50(10), 2140–2154 (2013)
Acknowledgements
This work is supported in part by Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics under Grant No. kfjj20161608, the National Natural Science Foundation of China under Grant No. 61672022, Key Disciplines of Computer Science and Technology od Shanghai Polytechnic University under Grant No. XXKZD1604, the Fundamental Research Funds for the Central Universities and Foundation of Graduate Innovation of Shanghai Polytechnic University.
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Chen, Y., Tan, W., Pan, Y. (2017). A Method Towards Community Detection Based on Estimation of Distribution Algorithm. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_57
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DOI: https://doi.org/10.1007/978-3-319-68542-7_57
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