全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

一种动态网络社区划分算法

Keywords: 动态网络,动态社区划分,数据挖掘

Full-Text   Cite this paper   Add to My Lib

Abstract:

基于MDL(minimumdescriptionlength)原则提出一种动态社区划分算法DCI(dynamiccommunityidentifi-cation).通过定义相邻时刻静态社区的演化关系,使得DCI算法不仅能发现具有不同生命周期的动态社区,并且所得结果能准确描述动态社区的演化过程.实验结果表明,与已有算法相比,DCI算法可以更加准确地划分动态社区,并在可接受的时间内完成较大规模动态网络社区划分.

References

[1]  SUN J M,PHILIP S Y,FALOUTSOS C,et al.GraphScope:parameter-free mining of large time-evolving graphs[C]∥Proceedings of Knowledge Discovery in Databases:KDD.New York:ACM,2007:687-696.
[2]  CHAYANT T,TANYA B W,DAVID K.A framework for community identification in dynamic social networks[C]∥Proceedings of Knowledge Discovery in Databases:KDD.New York:ACM,2007:717-726.
[3]  ASUR S,PARHASARATHY S,UCAR D.An event-based framework for characterizing the evolutionary behavior ofinteraction graphs[C]∥Proceedings of Knowledge Discovery in Databases:KDD.New York:ACM,2007:913-921.
[4]  GUHA S,GUNOPULOS D,KOUDAS N.Correlating synchronous and asynchronous data streams[C]∥Proceedings ofKnowledge Discovery in Databases:KDD.New York:ACM,2003:529-534.
[5]  PAPADIMITRIOU S,SUN J,FALOUTSOS C.Streaming pattern discovery in multiple time-series[C]∥Proceedings of VeryLarge Data Base.New York:ACM,2005:697-708.
[6]  SUN J,TAO D,FALOUTSOS C.Beyond streams and graphs:dynamic tensor analysis[C]∥Proceedings of KnowledgeDiscovery in Databases:KDD.New York:ACM,2006:374-383.
[7]  TONG H H,PAPADIMITRIOU S.Colibri:fast mining of large static and dynamic graphs[C]∥Proceedings of KnowledgeDiscovery in Databases:KDD.New York:ACM,2008:686-694.
[8]  RISSANEN J.A universal prior for integers and estimation by minimum description length[J].Annals of Statistics,1983,11(2):416-431.
[9]  STREHL A,GHOSH J.Cluster ensembles:a knowledge reuse framework for combining multiple partitions[J].Journal ofMachine Learning Research,2003(3):583-617.
[10]  LEY Michael.The DBLP computer science bibliography:computer theory,machine learning and data mining[DB/OL].[2008-10-10].http:∥www.informatik.uni-trier.de/~ley/db/.
[11]  NEWMAN M E J,GIRVAN M.Finding and evaluating community structure in networks[J].Physical Review E,2004,69(2):56-68.
[12]  PALLA G,DERENYI I,FARKAS I,et al.Uncovering the overlapping community structure of complex networks in nature andsociety[J].Nature,2005,435:814-818.
[13]  WAN L,LIAO J X,ZHU X M.CDPM:finding and evaluating community structure in social networks[C]∥Proceedings ofAdvanced Data Mining Applications.Berlin:Springer,2008:620-627.
[14]  STEVE G.An algorithm to find overlapping community structure in networks[C]∥Proceedings of Knowledge Discovery inDatabases:PKDD.Berlin:Springer,2007:593-600.
[15]  NEWMAN M E J.Fast algorithm for detecting community structure in networks[J].Physical Review E,2004,69(6):066133.
[16]  DHILLON S,MALLELA S,MODHA D S.Information-theoretic co-clustering[C]∥Proceedings of Knowledge Discovery inDatabases:KDD.New York:ACM,2003:89-98.
[17]  CHAKRABARTI D,PAPADIMITRIOU S,MODHA D S,et al.Fully automatic cross-associations[C]∥Proceedings ofKnowledge Discovery in Databases:KDD.New York:ACM,2004:79-88.
[18]  NOBLE C C,COOK D J.Graph-based anomaly detection[C]∥Proceedings of Knowledge Discovery in Databases:KDD.New York:ACM,2003:631-636.
[19]  KEOGH E,LONARDI S,RATANAMAHATANA C A.Towards parameter-free data mining[C]∥Proceedings of KnowledgeDiscovery in Databases:KDD.New York:ACM,2004:206-215.
[20]  NING H,XU W,CHI Y,et al.Incremental spectral clustering with application to monitoring of evolving blog communities[C]∥Proceedings of SIAM International Conference on Data Mining.Philadelphia:SIAM,2007:261-271.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133