期刊文献+

基于统计推理的社区发现模型综述 被引量:4

Overview of Community Detection Models on Statistical Inference
下载PDF
导出
摘要 社区有助于揭示复杂网络结构和个体间的关系。研究人员从不同视角提出很多社区发现方法,用来识别团内紧密、团间稀疏的网络结构。自2006年以来,提出了一些基于统计推理的社区发现方法,它们可识别实际网络中更多的潜在结构,并以其可靠的理论基础和优越的结构识别能力成为当前的主流。该类方法的主要目标是建立符合实际网络的生成模型以拟合观测网络,将社区发现问题转化为贝叶斯推理问题。首先给出社区发现中生成模型的相关定义;其次按照模型中社区组成元素将已有统计推理模型分为节点社区推理模型和链接社区推理模型,并深入探讨各种模型的设计思想及实现算法;再次,总结各模型适用的网络类型及规模、发现的社区结构、算法复杂度等,给出一种选择已有基于统计推理的社区发现模型的方法,并利用基准数据集对已有典型统计推理模型进行验证及分析;最后探讨了基于统计推理模型的社区发现存在的主要问题和未来发展的方向。 Community detection can identify salient structure and relations among individuals from the complex network.Researchers put forward many different methods,which are mainly used to detect the groups with dense connections within groups but sparser connections between them.To detect more latent structures in reality networks,various models on statistical inference have been proposed since 2006,which are on sound theoretical principles and have better performances identifying structures,and have become the state-of-the-art models.These models’ aims are to define a generative process to fit the observed network,and transfer the community detecting problem to Bayesian inference.First,the concepts on generation model were defined.Then,the article divided the generation models on community detection into vertex community and link community based on composition in community,and discussed design ideas and algorithms of each model in detail.What these models adapt to was also summarized from aspects of network type and scale,community structure,complexity etc,and then a method was given on how to select an existed statistical model.The existing classical models were tested and analyzed on the popular benchmark datasets.In the end,main problems on these models were highlighted,as well as the future progress.
出处 《计算机科学》 CSCD 北大核心 2012年第8期1-7,30,共8页 Computer Science
基金 国家自然科学基金项目(61033013) 北京市自然科学基金(4112046) 河北省自然科学基金项目(F2008000204)资助
关键词 社区发现 概率模型 随机块模型 统计推理 混合隶属度 Community detection Probabilistic model Stochastic block model Statistical inference Mixed membership
  • 相关文献

参考文献10

  • 1Yang T, Chi Y, Zhu S, et al. Directed network community detec- tion: A popularity and productivity link model [C]///SDM.2010 : 742-753. 被引量:1
  • 2Nallapati R M, Ahmed A, Xing E P, et al. Joint latent topic mo- dels for text and citations [C]///ACM. 2008:542-550. 被引量:1
  • 3Clauset A,Moore C,Newrnan M E J. Hierarchical structure and the prediction of missing links in networks [J]. Nature, 2008, 453(7191):98-101. 被引量:1
  • 4Cohn D, Chang H. Learning to probabilistically identify authori- tative documents [C]//Citeseer. 2000:167-174. 被引量:1
  • 5Yang T, J in R, Chi Y, et al. Combining link and content for com- munity detection: a discriminative approach [C]//KDD. 2009 : 927-936. 被引量:1
  • 6Hofmann T. Probabilistic latent semantic indexing [C]//ACM. 1999 : 50-57. 被引量:1
  • 7Yang T, Jin R, Chi Y, et al. A Bayesian framework for communi- ty detection integrating content and link [C]//BUAI. 2009:615- 622. 被引量:1
  • 8骆志刚,丁凡,蒋晓舟,石金龙.复杂网络社团发现算法研究新进展[J].国防科技大学学报,2011,33(1):47-52. 被引量:76
  • 9程学旗,沈华伟.复杂网络的社区结构[J].复杂系统与复杂性科学,2011,8(1):57-70. 被引量:69
  • 10杨博,刘大有,LIU Jiming,金弟,马海宾.复杂网络聚类方法[J].软件学报,2009,20(1):54-66. 被引量:212

二级参考文献148

  • 1解(亻刍),汪小帆.复杂网络中的社团结构分析算法研究综述[J].复杂系统与复杂性科学,2005,2(3):1-12. 被引量:86
  • 2Watts D J, Strogatz SH. Collective dynamics of Small-World networks. Nature, 1998,393(6638):440-442. 被引量:1
  • 3Barabasi AL, Albert R. Emergence of scaling in random networks. Science, 1999,286(5439):509-512. 被引量:1
  • 4Barabasi AL, Albert R, Jeong H, Bianconi G. Power-Law distribution of the World Wide Web. Science, 2000,287(5461):2115a. 被引量:1
  • 5Albert R, Barabasi AL, Jeong H. The Internet's Achilles heel: Error and attack tolerance of complex networks. Nature, 2000, 406(2115):378-382. 被引量:1
  • 6Girvan M, Newman MEJ. Community structure in social and biological networks. Proc. of the National Academy of Science, 2002,9(12):7821-7826. 被引量:1
  • 7Guimera R, Amaral LAN. Functional cartography of complex metabolic networks. Nature, 2005,433(7028):895-900. 被引量:1
  • 8Palla G, Derenyi I, Farkas I, Vicsek T. Uncovering the overlapping community structures of complex networks in nature and society. Nature, 2005,435(7043):814-818. 被引量:1
  • 9Wilkinson DM, Huberman BA. A method for finding communities of related genes. Proc. of the National Academy of Science, 2004,101(Suppl.1):5241-5248. 被引量:1
  • 10Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. Defining and identifying communities in networks. Proc. of the National Academy of Science, 2004,101 (9):2658-2663. 被引量:1

共引文献343

同被引文献41

  • 1汪小帆,李翔,陈关荣.复杂网络理论及应用[M].北京:清华大学出版社,2006. 被引量:25
  • 2WASSEMAN S, FAUST K. Social network analysis [ M]. Cam- bridge: Cambridge University Press, 1994:32 - 35. 被引量:1
  • 3WATIS D J, STROQATZ S H. Collective dyramics of 'small-world' networks [ J]. Nature, 1998, 393(6684) : 440 -442. 被引量:1
  • 4NEWMAN M E J. The structure of scientific collaboration networks [ J]. The Structure of Scientific Collaboration Networks, 2001, 98 (2) : 404 - 409. 被引量:1
  • 5WILLIAMS R J, MARTINEZ N D. Simple rules yield complex food Webs[ J]. Nature, 2000, 404(6774) : 180 - 183. 被引量:1
  • 6FELL D A, WAGNER A. The small world of metabolism [ J]. Na- ture Biotechnology, 2000, 18(11) : 1121 - 1122. 被引量:1
  • 7FALOUTSOS M, FALOUTSOS P, FALOUTSOS C. On power-law relationships of the lntcrnet topology [ J]. Computer Communications Review, 1999, 29:251 - 262. 被引量:1
  • 8NEWMAN M E J. Fast algorithm for detecting community structure in networks [ J]. Physical Review E, 2004, 69(6) : 1 - 5. 被引量:1
  • 9ZHANG H, KE K, LI W, et al. Grahical models based hierarchi- cal probabilistic community discovery in large-scale social networks [ J]. International Journal of Data Mining, Modelling and Manage- ment, 2010, 2(2): 95-116. 被引量:1
  • 10LIU Y, LUO J, YANG H, et al. Finding closely communicating community based on ant colony clustering model [ C]// Proceed-ings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence. Piscataway: IEEE Press, 2010: 127 - 131. 被引量:1

引证文献4

二级引证文献9

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部