计算机科学 ›› 2017, Vol. 44 ›› Issue (Z6): 407-413.doi: 10.11896/j.issn.1002-137X.2017.6A.092
边宅安,李慧嘉,陈俊华,马雨晗,赵丹
BIAN Zhai-an, LI Hui-jia, CHEN Jun-hua, MA Yu-han and ZHAO Dan
摘要: 近年来属性图聚类受到了广泛关注,其目的是将属性图中的节点划分到若干簇中,使得每一个集群都有紧密的簇内结构和均匀的属性值。现有的理论主要是假设属性图中的节点或对象是为了协助优化某个给定的方程,而忽略了它们在现实生活中本身的属性。同时,一些开放性问题尚未得到有效解决,如异构信息集成、计算成本高等。为此,把属性图聚类问题理解为自身节点代理的集群形成博弈。为了有效地整合拓扑结构和属性信息,提出了基于紧密性和均匀性约束的节点代理策略选择。进一步证明了博弈过程将会收敛到弱帕累托纳什均衡。在实证方面,设计了一个分布式和异构的多智能体系统,给出了一个快速的分布式学习算法。该算法的主要特点是结果分区的重叠率可以由一个事先给定的阈值控制。最后,在现实社交网络上进行了模拟实验,并与目前先进方法进行比较,结果证实了所提算法的有效性。
[1] BOTHOREL C,CRUZ J D,MAGNANI M,et al.Clustering attributed graphs:Models,measures and methods[J].Network Science,2015,3(3):408-444. [2] LI Q,ZHI Q,WANG M.Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks[J].Journal of Software,2006,29(12):2230-2237. [3] Fortunato S.Community detection in graphs[J].Physics Reports,2010,486(3-5):75-174. [4] CLAUSET A,NEWMAN M E,MOORE C.Finding communitystructure in very large networks[J].Physical Review E, 2005,70(6Pt 2):264-277. [5] BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fastunfolding of communities in large networks[J].Journal of Statistical Mechanics Theory & Experiment,2008,2008(10):155-168. [6] BARTHELEMY M,FORTUNATO S.Resolution limit in community detection[J].Proceedings of the National Academy of Sciences of the United States of America,2007,104(1):36-41. [7] ALDECOA R,MARN I.Surprise maximization reveals the com-munity structure of complex networks[J].Scientific Reports,2013,3(1):173-185. [8] CLAUSET A.Finding local community structure in networks[J].Physical Review E Statistical Nonlinear&Soft Matter Phy-sics,2005,72(2):254-271. [9] LUO F,WANG J Z,PROMISLOW E.Exploring local community structures in large networks[J].Web Intelligence & Agent Systems,2006,6(4):387-400. [10] HUANG J,SUN H,LIU Y,et al.Towards online multiresolution community detection in large-scale networks[J].Plos One,2011,6(8):492-492. [11] LI K,PANG Y.A vertex similarity probability model for finding network community structure[C]∥PAKDD.2012:456-467. [12] CHEN H H,GOU L,ZHANG X,et al.Discovering missinglinks in networks using vertex similarity measures[C]∥ACM Symposium on Applied Computing.2012:138-143. [13] COMBE D,LARGERON C,E GYED-ZSIGMOND E,et al.Combining relations and text in scientific network clustering[C]∥International Conference on Advances in Social Networks Ana-lysis and Mining.2012:1248-1253. [14] ZHOU Y,CHENG H,YU J X.Graph Clustering Based onStructural/Attribute Similarities[J].Proceedings of the Vldb Endowment,2009,2(1):718-729. [15] CHENG H,ZHOU Y,YU J X.Clustering large attributedgraphs:A balance between structural and attribute similarities[J].ACM Transactions on Knowledge Discovery from Data,2011,5(2):190-205. [16] ZHOU Y,CHENG H,YU J X.Clustering large attributedgraphs:An efficient incremental approach[C]∥2013 IEEE 13th International Conference on Data Mining.2010:689-698. [17] III J J P,MORENO S,FOND T L,et al.Attributed graph mo-dels:Modeling network structure with correlated attributes[C]∥Proceedings of the 23rd International Conference on World Wide Web.ACM,2014:831-842. [18] ZANGHI H,VOLANT S,AMBROISE C.Clustering based on random graph model embedding vertex features[J].Pattern RecognitionLetters,2010,31(9):830-836. [19] XU Z,KE Y,WANG Y,et al.A model-based approach to attri-buted graph clustering[C]∥Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.ACM,2012:505-516. [20] GUNNEMANN S,FARBER I,BODEN B,et al.Subspace clustering meets dense subgraph mining:A synthesis of two paradigms[C]∥IEEE International Conference on Data Mining.2010:845-850. |
No related articles found! |
|