《计算机应用研究》|Application Research of Computers

基于云聚合理论的城市社区划分算法研究

Urban community classification algorithm based on cloud aggregation theory

免费全文下载 (已被下载 次)  
获取PDF全文
作者 顾宏博,徐名海,奚杰杰,吴晶
机构 南京邮电大学,南京 210003
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2017)01-0036-06
DOI 10.3969/j.issn.1001-3695.2017.01.007
摘要 城市可以看做是由若干社区构成的一种特殊的社会网络,合理有效的城市社区划分不仅能够提高居民的生活质量,同时也有助于管理部门更好地实现城市的管理,以弥补现有城市规划存在的基础设施分配不健全等问题。根据云的形成过程提出一种创新的基于云聚合理论的城市社区划分算法,将社区节点作为个体,以水蒸气聚合成云、云重组过程为理论支撑,对节点进行逐步凝聚划分及重组,最终达到均衡稳定的状态。为验证算法的可行性,在MATLAB实验平台上进行仿真。显示提出算法的模块度量Q值为0.6572,较高于同类凝聚算法的模块度量值0.6121,表明所提算法的性能优于同类的凝聚算法,能较好地反映真实的城市社区结构,此外还能够获取优于现实社区的划分结果,更好地服务于居民和管理部门。
关键词 城市社区划分;云聚合理论;模块度量值;凝聚算法
基金项目 国家自然科学基金资助项目(702710456)
本文URL http://www.arocmag.com/article/01-2017-01-007.html
英文标题 Urban community classification algorithm based on cloud aggregation theory
作者英文名 Gu Hongbo, Xu Minghai, Xi Jiejie, Wu Jing
机构英文名 NanjingUniversityofPosts&Telecommunications,Nanjing210003,China
英文摘要 An urban can be regarded as a social network, which is made up of a number of communities.A reasonable and effective community classification can not only improve residents’ life quality, but also contribute to the better management of an urban.This paper developed a new classification algorithm based on cloud aggregation theory.The algorithm community nodes gradually aggregated and reorganize, ultimately achieved a balanced state by the theory.Experimental results indicate that modularity of the proposed algorithm is 0.6572, which is higher than the similar algorithm’s modularity 0.6121.Comparing with the classical algorithms and the real urban community structure, results demonstrate that this algorithm is better than the others, and it can reflect the real urban structure.
英文关键词 urban community division; cloud aggregation theory; modularity; aggregation algorithm
参考文献 查看稿件参考文献
  [1] Dutt S. New faster Kernighan-Lin-type graph-partitioning algorithms[C] //Proc of IEEE/ACM International Conference on Computer-Aided Design. Los Alumitos:IEEE Computer Society Press, 1993:370-377.
[2] Manna K, Choubey V, Chattopadhyay S, et al. Thermal variance-aware application mapping for mesh based network-on-chip design using Kernighan-Lin partitioning[C] //Proc of International Conference on Parallel, Distributed and Grid Computing. 2014:274-279.
[3] Ruan Jianhua, Zhang Weixiong. An efficient spectral algorithm for network community discovery and its applications to biological and social networks[C] //Proc of the 7th IEEE International Conference on Data Mining. Washington DC:IEEE Computer Society, 2007:643-648.
[4] Gou Shuiping, Zhuang Xiong. Parallel sparse spectral clustering for SAR image segmentation[J] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(4):1949-1963.
[5] Wang Huiqing, Chen Junjie, Huang Shaobin, et al. A heuristic initialization-independent spectral clustering[C] //Proc of the 5th International Conference on Internet Computing for Science and Engineering. Washington DC:IEEE Computer Society, 2010:81-84.
[6] Ono H. Fast random walks on finite graphs and graph topological information[C] //Proc of the 2nd International Conference on Networking and Computing. Washington DC:IEEE Computer Society, 2011:360-363.
[7] Palla G, Derenyi I, Farkas I, et al. Uncovering the overlapping community structure of complex networks in nature and society[J] . Nature, 2005, 435(7043):814-818.
[8] Palla G, Farkas I, Pollner P, et al. Directed network modules[J] . New Journal of Physical, 2007, 9(6):186.
[9] Newman M E J. Fast algorithm for detecting community structure in networks[J] . Physical Review E, 2004, 69(6):066133.
[10] Girvan M, Newman M E J. Community structure in social and biological networks[J] . Proceedings of the National Academy of Sciences of the USA, 2001, 99(12):7821-7826.
[11] Tyler J, Wilkinson D, Huberman B. Email as spectroscopy:automated discovery of community structure within organizations[C] //Proc of the 1st International Conference on Communities and Technologies. 2003:81-96.
[12] Radicchi F, Castellano C, Cecconi F, et al. Defining and identifying communities in networks[J] . Proceedings of the National Academy of Sciences of the USA, 2004, 101(9):2658-2663.
[13] Chen Guoqiang, Guo Xiaofang. A genetic algorithm based on modularity density for detecting community structure in complex networks[C] //Proc of International Conference on Computational Intelligence and Security. Washington DC:IEEE Computer Society, 2011:151-154.
[14] Clauset A, Newman M E J, Moore C. Finding community structure in very large networks[J] . Physical Review E, 2005, 70(6):066111.
[15] Dinh T N, Thai M. Finding community structure with performance guarantees in scale-free networks[C] //Proc of IEEE International Conference on Privacy, Security, Risk, and Trust. 2011:888-891.
[16] Wang Xiaohan, Chen Zhaoqun. Edge balance ratio:power law from vertices to edges in directed complex network[J] . IEEE Journal of Selected Topics in Signal Processing, 2013, 7(2):184-194.
[17] Tang Jin, Jiang Bo, Chang Chinchen, et al. Graph structure analysis based on complex network[J] . Digital Signal Processing, 2012, 22(5):713-725.
[18] 李孝伟, 陈福才, 刘力雄. 一种融合节点与链接属性的社交网络社区划分算法[J] . 计算机应用研究, 2013, 30(5):1477-1480.
收稿日期 2015/10/20
修回日期 2015/12/8
页码 36-41
中图分类号 TP301.6
文献标志码 A