计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100002-7.doi: 10.11896/jsjkx.211100002
原慧琳1, 韩真2, 冯宠2, 黄碧2, 刘军涛2
YUAN Hui-lin1, HAN Zhen2, FENG Chong2, HUANG Bi2, LIU Jun-tao2
摘要: 社区发现是复杂网络研究领域的一个热点问题,目前已经有许多局部社区发现算法被提出用于快速发现高质量的社区,不过它们往往存在种子节点依赖或是稳定性问题。因此,部分算法试图根据核心节点被邻居高度包围且相互之间距离较远的拓扑特性来精确地锁定种子节点以避免上述问题,但距离的计算使得其时间复杂度较高。文中提出了一种基于核心节点影响力的社区发现方法CDIC,该方法首先根据核心节点的拓扑特性和网络邻接信息寻找所有可能是核心的节点,之后利用真正核心节点影响力较高的性质和标签传播的思想来扩张社区,并淘汰被误选为核心的节点以避免种子依赖问题,同时不涉及最短距离的计算也保证了较低的时间复杂度,最后依据相似度理论提出了一种社区对节点的吸引力来合并特异节点,以保证算法结果的稳定性。将CDIC与6种经典算法以及2种近年来提出的算法在64个人工网络和4个真实网络上进行仿真实验,并对其社区划分结果对应的标准化互信息值和纯度进行了比较,结果表明了CDIC的有效性。
中图分类号:
[1]SUN Z,SUN Y,CHANG X,et al.Community detection based on the Matthew effect[J].Knowledge-Based Systems,2020,205:106256. [2]GIRVAN M,NEWMAN M E J.Community structure in social and biological networks[J].Proceedings of the National Academy of Sciences,2002,99(12):7821-7826. [3]ZHANG Y,LIU Y,LI Q,et al.Lilpa:A label importance based label propagation algorithm for community detection with application to core drug discovery[J].Neurocomputing,2020,413:107-133. [4]CHEN Z,XIE Z,ZHANG Q.Community detection based on local topological information and its application in power grid[J].Neurocomputing,2015,170:384-392. [5]BAHULKAR A,SZYMANSKI B K,BAYCIK N O,et al.Community detection with edge augmentation in criminal networks[C]//2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining(ASONAM).IEEE,2018:1168-1175. [6]ALSINI A,DATTA A,HUYNH D Q,et al.Community aware personalized hashtag recommendation in social networks[C]//Australasian Conference on Data Mining.Singapore:Springer,2018:216-227. [7]ZHAO W J,ZHANG F B,LIU J L,Research progress of complex network community discovery[J].Computer Science,2020,47(2):10-20. [8]NEWMAN M E J.Fast algorithm for detecting communitystructure in networks[J].Physical Review E,2004,69(6):066133. [9]YUAN Q,LIU B.Community detection via an efficient nonconvex optimization approach based on modularity[J].Computational Statistics & Data Analysis,2021,157:107163. [10]GUIMERA R,AMARAL L A N.Functional cartography ofcomplex metabolic networks[J].Nature,2005,433(7028):895-900. [11]SHANG R,BAI J,JIAO L,et al.Community detection based on modularity and an improved genetic algorithm[J].Physica A,2013,392(5):1215-1231. [12]FORTUNATO S.Community detection in graphs[J].Physics Reports,2010,486(3/4/5):75-174. [13]NEWMAN M E J.Finding community structure in networksusing the eigenvectors of matrices[J].Physical Review E,2006,74(3):036104. [14]NEWMAN M E J,LEICHT E A.Mixture models and exploratory analysis in networks[J].Proceedings of the National Aca-demy of Sciences,2007,104(23):9564-9569. [15]PONS P,LATAPY M.Computing communities in large net-works using random walks[C]//International Symposium on Computer and Information Sciences.Berlin:Springer,2005:284-293. [16]FANRONG M,MU Z,YONG Z,et al.Local Community Detection in Complex Networks Based on Maximum Cliques Extension [J].Mathematical Problems in Engineering,2014(2014):1-12. [17]QI X,TANG W,WU Y,et al.Optimal local community detection in social networks based on density drop of subgraphs[J].Pattern Recognition Letters,2014,36:46-53. [18]BERAHMAND K,BOUYER A,VASIGHI M.Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes[J].IEEE Transactions on Computational Social Systems,2018,5(4):1021-1033. [19]LIU S,XIA Z.A two-stage BFS local community detection algorithm based on node transfer similarity and Local Clustering Coefficient[J].Physica A:Statistical Mechanics and its Applications,2020,537:122717. [20]RAGHAVAN U N,ALBERT R,KUMARA S.Near linear time algorithm to detect community structures in large-scale networks[J].Physical Review E,2007,76(3):036106. [21]BAGROW J P,BOLLT E M.Local method for detecting communities[J].Physical Review E,2005,72(4):046108. [22]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496. [23]YOU X,MA Y,LIU Z.A three-stage algorithm on community detection in social networks[J].Knowledge-Based Systems,2020,187:104822. [24]DENG Z H,QIAO H H,GAO M Y,et al.Complex networkcommunity detection method by improved density peaks model[J].Physica A:Statistical Mechanics and its Applications,2019,526:121070. [25]HENNIG C,HAUSDORF B.Design of dissimilarity measures:A new dissimilarity between species distribution areas[M]//Data Science and Classification.Berlin:Springer,2006:29-37. [26]CLAUSET A,NEWMAN M E J,MOORE C.Finding community structure in very large networks[J].Physical Review E,2004,70(6):066111. [27]BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,2008(10):P10008. [28]PAN X,XU G,WANG B,et al.A novel community detection algorithm based on local similarity of clustering coefficient in social networks[J].IEEE Access,2019,7:121586-121598. [29]PARÉS F,GASULLA D G,VILALTAA,et al.Fluid communities:A competitive,scalable and diverse community detection algorithm[C]//International Conference on Complex Networks and Their Applications.Cham:Springer,2017:229-240. [30]SHANG R,ZHANG W,JIAO L.Circularly searching corenodes based label propagation algorithm for community detection[J].International Journal of Pattern Recognition and Artificial Intelligence,2016,30(8):1659024. [31]BERAHMAND K,BOUYER A.LP-LPA:A link influence-based label propagation algorithm for discovering community structures in networks[J].International Journal of Modern Physics B,2018,32(6):1850062. [32]YAKOUBI Z,KANAWATI R.LICOD:A Leader-driven algo-rithm for community detection in complex networks[J].Vietnam Journal of Computer Science,2014,1(4):241-256. [33]YIN C,ZHU S,CHEN H,et al.A method for community detection of complex networks based on hierarchical clustering[J].International Journal of Distributed Sensor Networks,2015,11(6):849140. [34]WANG X,LIU G,LI J,et al.Locating structural centers:A density-based clustering method for community detection[J].PloS One,2017,12(1):e0169355. [35]STREHL A,GHOSH J.Cluster ensembles-a knowledge reuseframework for combining multiple partitions[J].Journal of Machine Learning Research,2002,3(Dec):583-617. [36]ZHAO Y,KARYPIS G.Empirical and theoretical comparisons of selected criterion functions for document clustering[J].Machine Learning,2004,55(3):311-331. [37]LANCICHINETTI A,FORTUNATO S,RADICCHI F.Benchmark graphs for testing community detection algorithms[J].Physical Review E,2008,78(4):046110. [38]ZACHARY W W.An Information Flow Model for Conflict and Fission in Small Groups[J].Journal of Anthropological Research,1977,33(4):452-473. [39]LUSSEAU D,SCHNEIDER K,BOISSEAU O J,et al.The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations[J].Behavioral Ecology and Sociobiology,2003,54(4):396-405. [40]ADAMIC L A,GLANCE N.The political blogosphere and the 2004 US election:divided they blog[C]//Proceedings of the 3rd International Workshop on Link Discovery.2005:36-43. |
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