期刊文献+

Gnutella网络上的动态社区结构分析 被引量:1

Analysis of Dynamic Communities on Gnutella
下载PDF
导出
摘要 研究Gnutella网络中动态社区(Community)结构的演化问题.定义了有关社区和节点的关键事件.通过在实际Gnutella网络测量数据中检测关键事件的发生,发现网络中节点登陆和退出非常频繁.基于关键事件定义表征社区稳定性的指标,在Gnutella网络数据中的测量表明,节点频繁登陆和退出造成了网络上社区结构的不稳定.另一方面,定义了反映用户所属社区稳定性的指标,对Gnutella网络拓扑数据的测量结果显示,用户经常更换自己所属社区,同样归咎于节点的频繁变动. This paper focus on the community dynamics on Gnutella topology.We defined the events involving communities and nodes and monitored the dynamic of communities by detecting these events.By testing on Gnutella,we find that nodes frequently join or leave.We defined the index that reflects stability of communities.Base on the topological data measured on Gnutella,we find that the communities on Gnutella are not stable because of the frequent appearance and disappearance of nodes.Furthermore,we defined the index that reflects the stability of nodes′ community.Testing on the Gnutella topology show that,nodes that didn′t disappear would always tend to change their communities.This is also caused by the frequent appearance and disappearance on nodes.
出处 《小型微型计算机系统》 CSCD 北大核心 2012年第8期1655-1659,共5页 Journal of Chinese Computer Systems
基金 国家“八六三”高技术研究发展计划项目(2008AA01A317)资助 国家自然科学基金项目(60935001,61174124)资助
关键词 动态社区结构检测 GNUTELLA 演化过程 用户行为 dynamic community detection Gnutella evolution process user behavior
  • 相关文献

参考文献10

  • 1Newman M E J. Detecting community structure in networks [J]. European Physical Journal B, 2004, 38 (2) :321-330.
  • 2[EB/OL]http://crawler.trillinux.orgihistory.html, 2011.
  • 3Bolla R, Gaeta R, Magnetto A, et al. A measurement study supporting P2P file-sharing community models [J]. Computer Networks, 2009, 53 (4) :485-500.
  • 4Newman M E J, Girvan M. Finding and evaluating community structure in networks [J]. Physical Review E, 2004, 69 ( 2 ) : 026113- 026127.
  • 5Newman M E J. Fast algorithm for detecting community structure in networks[J]. Phys. Rev. E, 2004, 69(6) :066133-066137.
  • 6Clauset A, Newman M E J, Moore C. Finding community structure in very large networks [J]. Phys. Rev. E, 2004, 70 (6) : 066111-066115.
  • 7Schuetz P, Caflisch A. Efficient modularity optimization by multi step greedy algorithm and vertex mover refinement [J]. Phys. Rev. E, 2008, 77 (4) : 046112-046118.
  • 8Asur S, Parthasarathy S, Ucar D. An event-based framework for characterizing the evolutionary behavior of interaction graphs [C]. In: KDD '07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, 2007 :913-921.
  • 9Stutzbach D, Reza Rejaie, Nick Duffield, et al. On unbiased sampling for unstructured peer-to-peer networks [J]. IEEEI ACM Transactions on Networking,2009 ,17 (2) :377-390.
  • 10http://mirage.cs. uoregon. edu/P2P/snapshots. htrnl[EB/OL]. 2009.

同被引文献29

  • 1Scott J. Social Network Analysis: A Handbook. London: Sage Publications, 2000.
  • 2Ceren Budak, Divyakant Agrawal, Amr El Abbadi. Struc tural trend analysis for online social networks. Proceedings of the VLDB Endowment, 2011, 4(10) : 101-111.
  • 3Guo Jing-Feng, Zhang Chun-Ying. Attribute graph and its structure. ICICExpress Letters, 2011, 5(8)- 2611 -2616.
  • 4Zhang Chun-Ying, Guo ling-Feng, Research on random walk rough matching algorithm of attribute sub graph. Advanced Materials and Computer Science, 2011, 474(4) : 297-302.
  • 5Zhang Chun-Ying, Liang Rui-Tao. Set pair community situa- tion analysis and dynamic mining algorithms of web social network. ICICExpressLetters, 2011, 5(12):4519-4524.
  • 6Shiga M, Takigawa I, Mamitsuka H. A spectral clustering approach to optimally combining numerical vectors with a modular network//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2007:647- 656.
  • 7Newman M E J. Detecting community structure in networks. European Physical Journal(B), 2004, 38(2): 321-330.
  • 8Newman M E J. Fast algorithm for detecting community structure in networks. Physical Review E, 2004, 69 (6): 066033.
  • 9Guimera R, Amaral L A N. Functional cartography of com- plex metabolic networks. Nature, 2005, 433 (7028) : 895 -900.
  • 10Hanneman R, Riddle M. Introduction to social network methods [-Ph. D. dissertationS. University of California, Riverside, CA, 2005:20-30.

引证文献1

二级引证文献13

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