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基于免疫离散差分进化算法的复杂网络社区发现

张英杰 龚中汉 陈乾坤

张英杰, 龚中汉, 陈乾坤. 基于免疫离散差分进化算法的复杂网络社区发现. 自动化学报, 2015, 41(4): 749-757. doi: 10.16383/j.aas.2015.c140018
引用本文: 张英杰, 龚中汉, 陈乾坤. 基于免疫离散差分进化算法的复杂网络社区发现. 自动化学报, 2015, 41(4): 749-757. doi: 10.16383/j.aas.2015.c140018
ZHANG Ying-Jie, GONG Zhong-Han, CHEN Qian-Kun. Community Detection in Complex Networks Using Immune Discrete Differential Evolution Algorithm. ACTA AUTOMATICA SINICA, 2015, 41(4): 749-757. doi: 10.16383/j.aas.2015.c140018
Citation: ZHANG Ying-Jie, GONG Zhong-Han, CHEN Qian-Kun. Community Detection in Complex Networks Using Immune Discrete Differential Evolution Algorithm. ACTA AUTOMATICA SINICA, 2015, 41(4): 749-757. doi: 10.16383/j.aas.2015.c140018

基于免疫离散差分进化算法的复杂网络社区发现

doi: 10.16383/j.aas.2015.c140018
基金项目: 

国家自然科学基金(61440026),教育部博士点基金(20110161110035),湖南省自然科学基金重点项目(13JJA002)资助

详细信息
    作者简介:

    龚中汉 湖南大学信息科学与工程学院硕士研究生.2011年获得湖南大学信息科学与工程学院学士学位.主要研究方向为进化计算和数据挖掘.E-mail:hncl18@hnu.edu.cn

    通讯作者:

    张英杰 湖南大学信息科学与工程学院副教授.2005年获得湖南大学控制理论与控制工程博士学位.主要研究方向为智能控制,计算智能和节能优化控制.本文通信作者.E-mail:zhangyj@hnu.edu.cn

Community Detection in Complex Networks Using Immune Discrete Differential Evolution Algorithm

Funds: 

Supported by National Natural Science Foundation of China(61440026), Doctoral Fund of Ministry of Education of China(20110161110035), and Hunan Provincial Natural Science Foundation of China(13JJA002)

  • 摘要: 针对复杂网络社区发现问题,在标准差分进化算法的框架下,提出一种新型免疫离散差分进化算法(Immune discrete differential evolution, IDDE).该算法通过标签传播策略生成初始种群,采用离散差分进化策略来保证种群在问题空间的全局搜索能力,同时对种群中的优秀个体执行针对性的高频克隆变异操作,以提高算法的局部开发能力,改善算法的收敛性能.在计算机生成网络与真实世界网络中的仿真实验结果表明:IDDE算法具有较强的寻优性能与鲁棒性,能够有效探测复杂网络中存在的社区结构.
  • [1] Girvan M, Newman M E J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12):7821-7826
    [2] [2] Newman M E J, Girvan M. Finding and evaluating community structure in networks. Physical Review E, 2004, 69(2):026113
    [3] [3] Zhang S H, Wang R S, Zhang X S. Identification of overlapping community structure in complex networks using fuzzy means clustering. Physica A:Statistical Mechanics and Its Applications, 2007, 374(1):483-490
    [4] Huang Fa-Liang, Huang Ming-Xuan, Yuan Chang-An, Yao Zhi-Qiang. Spectral clustering ensemble algorithm for discovering overlapping communities in social networks. Control and Decision, 2014, 29(4):713-718(黄发良, 黄名选, 元昌安, 姚志强. 网络重叠社区发现的谱聚类集成算法. 控制与决策, 2014, 29(4):713-718)
    [5] [5] Raghavan U N, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale network. Physical Review E, 2007, 76(3):036106
    [6] Jin Di, Liu Jie, Yang Bo, He Dong-Xiao, Liu Da-You. Genetic algorithm with local search for community detection in large-scale complex networks. Acta Automatica Sinica, 2011, 37(7):873-882(金弟, 刘杰, 杨博, 何东晓, 刘大有. 局部搜索与遗传算法结合的大规模复杂网络社区探测. 自动化学报, 2011, 37(7):873-882)
    [7] [7] Shang R H, Bai J, Jiao L C, Jin C. Community detection based on modularity and an improved genetic algorithm. Physica A:Statistical Mechanics and Its Applications, 2013, 392(5):1215-1231
    [8] Huang Fa-Liang, Zhang Shi-Chao, Zhu Xiao-Feng. Discovering network community based on multi-objective optimization. Journal of Software, 2013, 24(9):2062-2077(黄发良, 张师超, 朱晓峰. 基于多目标优化的网络社区发现方法. 软件学报, 2013, 24(9):2062-2077)
    [9] [9] Gong M G, Cai Q, Chen X W, Ma L J. Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Transactions on Evolutionary Computation, 2014, 18(1):82-97
    [10] Luo Zhi-Gang, Ding Fan, Jiang Xiao-Zhou, Shi Jin-Long. New progress on community detection in complex networks. Journal of National University of Defense Technology, 2011, 33(1):47-52(骆志刚, 丁凡, 蒋晓舟, 石金龙. 复杂网络社团发现算法研究新进展. 国防科技大学学报, 2011, 33(1):47-52)
    [11] Brandes U, Delling D, Gaertler M, Goerke R, Hoefer M, Nikoloski Z, Wagner D. Maximizing modularity is hard. arXiv:physics/0608255, 2006.
    [12] Guimer R, Sales-Pardo M, Amaral L A N. Modularity from fluctuations in random graphs and complex network. Physical Review E, 2004, 70(2):025101
    [13] Huang Fa-Liang, Xiao Nan-Feng. Particle-swarm-optimization algorithm to discover network community. Control Theory and Application, 2011, 28(9):1135-1140(黄发良, 肖南峰. 网络社区发现的粒子群优化算法. 控制理论与应用, 2011, 28(9):1135-1140)
    [14] Jia G B, Cai Z X, Musolesi M, Wang Y, Tennant D A, Weber R J, Heath J K, He S. Community detection in social and biological networks using differential evolution. In:Proceedings of the 6th International Conference on Learning and Intelligent Optimization Conference LION6. Heidelberg:Springer, 2012. 71-85
    [15] Wolpert D H, Macready W G. No free lunch theorems for optimization. IEEE Transaction on Evolutionary Computation, 1997, 2(1):62-87
    [16] Santo F. Community detection in graphs. Physics Reports, 2010, 486(3-5):75-174
    [17] Fortunato S, Barthelemy M. Resolution limit in community detection. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(1):36-41
    [18] Storn R, Price K. Differential evolution:a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4):341-359
    [19] Karabulut K, Tasgetiren M F. A discrete artificial bee colony algorithm for the traveling salesman problem with time windows. In:Proceedings of the 2012 IEEE Congress Evolutionary Computation. Piscataway, NJ:IEEE, 2012. 1-7
    [20] Pan Q K, Mehmet F T, Liang Y C. A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Computers Industrial Engineering, 2008, 55(4):795-816
    [21] Tasgin M, Herdagdelen A, Bingol H. Community detection in complex networks using genetic algorithms. arXiv:0711.0491, 2007.
    [22] Clauset A, Newman M E J, Moore C. Finding community structure in very large networks. Physical Review E, 2004, 70(6):066111
    [23] Gong M G, Fu B, Jiao L C, Du H F. Memetic algorithm for community detection in networks. Physical Review E, 2011, 84(5):056101
    [24] Lancichinetti A, Fortunato S, Radicchi F. Benchmark graphs for testing community detection algorithms. Physical Review E, 2008, 78(4):046110
    [25] Danon L, Daz-Guilera A, Duch J, Arenas A. Comparing community structure identification. Journal of Statistical Mechanics:Theory and Experiment, 2005, 2005(09):P09008
    [26] Derrac J, Garca S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 2011, 1(1):3-18
    [27] Zachary W W. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 1997, 33(4):452-473
    [28] Lusseau D, Schneider K, Boisseau O J, Haase P, Slooten E, Dawson S M. The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology, 2003, 54(4):396-405
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出版历程
  • 收稿日期:  2014-01-10
  • 修回日期:  2014-12-02
  • 刊出日期:  2015-04-20

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