基于Louvain算法的铁路旅客社会网络社区划分研究
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摘要
为了对铁路旅客社会网络结构进行更深入的分析,需要利用社区划分算法提取出联系紧密的旅客出行团体。由于铁路旅客社会网络规模庞大,常规的社区划分算法处理速度非常慢,甚至无法处理。本文在利用铁路旅客出行大数据构建旅客社会网络的基础上,选择Louvain算法对铁路旅客社会网络进行社区划分。分析结果表明,Louvain算法能够对铁路旅客社会网路进行快速有效地社区划分,划分的社区中节点紧密程度较高,且都具有小世界特性。
In order to further analyze the social network structure of railway passengers, it is necessary to use the community detection algorithm to extract the closely connected passenger groups. The scale of railway passenger social network is huge,and the conventional community detect algorithm is very slow to deal with or even unable to handle. On the basis of constructing the passenger social network by using the railway passenger travel big data, this paper chooses Louvain algorithm to detect the community of railway passenger social network. The analysis results show that the Louvain algorithm can detect the railway passenger social network rapidly and effectively, the nodes in communities have a high degree of closeness and all the communities have small-world characteristics.
In order to further analyze the social network structure of railway passengers, it is necessary to use the community detection algorithm to extract the closely connected passenger groups. The scale of railway passenger social network is huge,and the conventional community detect algorithm is very slow to deal with or even unable to handle. On the basis of constructing the passenger social network by using the railway passenger travel big data, this paper chooses Louvain algorithm to detect the community of railway passenger social network. The analysis results show that the Louvain algorithm can detect the railway passenger social network rapidly and effectively, the nodes in communities have a high degree of closeness and all the communities have small-world characteristics.
引文
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[4]Barnard ST,Simon HD.Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems[J].Concurrency&Computation Practice&Experience,1994,6(2):101-117
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[6]Newman M.Modularity and community structure in networks[C]//APS March Meeting.American Physical Society,2006:8577-8582
[7]Lambiotte R.Multi-scale Modularity and Dynamics in Complex Networks[M].New York:Springer,2013:125-141
[8]Lambiotte R,Delvenne JC,Barahona M.Laplacian Dynamics and Multiscale Modular Structure in Networks[J].IEEE Transactions on Network Science and Engineering,2015,1(2):76-90
[9]吴祖峰,王鹏飞,秦志光,等.改进的Louvain社团划分算法[J].电子科技大学学报,2013(1):105-108
[10]吴卫江,李沐南,李国和.Louvain算法的并行化处理[J].计算机与数字工程,2016,44(8):1402-1406
[11]林定,徐颖,黄国新,等.基于Louvain算法的图数据三维树形可视化[J].计算机工程与应用,2018,54(7):96-101
[13]Watts DJ,Strogatz SH.Collective dynamics of‘small-world’networks[J].Nature,1998,393:440-442
[2]Farrugia M,Quigley A.Enhancing Airline Customer Relationship Management Data by Inferring Ties between Passengers[C]//2009 International Conference on Computational Science and Engineering.2009:795–800
[3]Blondel VD,Guillaume JL,Lambiotte R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics,2008(10):155-168
[4]Barnard ST,Simon HD.Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems[J].Concurrency&Computation Practice&Experience,1994,6(2):101-117
[5]Karypis G,Kumar V.A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs[J].SIAM Journal on Scientific Computing,1999,20(1):359-392
[6]Newman M.Modularity and community structure in networks[C]//APS March Meeting.American Physical Society,2006:8577-8582
[7]Lambiotte R.Multi-scale Modularity and Dynamics in Complex Networks[M].New York:Springer,2013:125-141
[8]Lambiotte R,Delvenne JC,Barahona M.Laplacian Dynamics and Multiscale Modular Structure in Networks[J].IEEE Transactions on Network Science and Engineering,2015,1(2):76-90
[9]吴祖峰,王鹏飞,秦志光,等.改进的Louvain社团划分算法[J].电子科技大学学报,2013(1):105-108
[10]吴卫江,李沐南,李国和.Louvain算法的并行化处理[J].计算机与数字工程,2016,44(8):1402-1406
[11]林定,徐颖,黄国新,等.基于Louvain算法的图数据三维树形可视化[J].计算机工程与应用,2018,54(7):96-101
[13]Watts DJ,Strogatz SH.Collective dynamics of‘small-world’networks[J].Nature,1998,393:440-442