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面向微博影响力的社交网络特征分析 被引量:5

Analysis of characteristics of social networks in terms of microblog impact
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摘要 社交网络的影响力与其自身的结构特征密切相关。基于新浪微博的数据,对用户的粉丝数、关注数的分布及这些特征之间的关系进行分析,发现用户的粉丝数、关注数、微博数都符合幂律分布;探讨了节点之间的距离特征,发现并证明了微博网络中存在着"小世界"现象;研究了节点之间的链接形成问题,发现链接的形成满足三元闭包原理。以上三方面研究结果,对于探索微博影响力同底层社交网络结构特征的关系、设计微博影响力控制机制具有重要的意义。 The influence of social network is closely related with its structural characteristics. Based on the data from Sina microblog, the distributions of the number of followers and followings were analyzed and found that the number of followers and followings both were power-law distributed. The distance characteristic between different pairs of nodes was discussed, and it was found and proved that there was "small-world" phenomenon in the microblog network. At last, the links between nodes in the network were investigated and found that the formation of the link satisfied triple closure principle. The investigation results on the above three topics are important for us to explore the relationship between the influence of micro- blog and the structural characteristics of its underlying social network, as well as to the design of mechanisms to control the influence.
出处 《计算机应用》 CSCD 北大核心 2013年第12期3359-3362,3418,共5页 journal of Computer Applications
基金 国家自然科学基金资助项目(60973107) 网络文化与数字传播北京市重点实验室资助项目(ICDD201106 ICDD201207) 国家社会科学基金重大项目(12&ZD234)
关键词 影响力 幂律分布 小世界 三元闭包 influence power law distribution small-world triple closure
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参考文献14

  • 1GUILLE A, HACID H. A predictive model for the temporal dynam- ics of information diffusion in online social networks [ C ]// WWW'12 Companion: Proceedings of the 21st International Confer- ence Companion on World Wide Web. New York: ACM, 2012: 1145 - 1152.
  • 2KEMPE D, KLEINBERG J, TARDOS t. Maximizing the spread of influence through a social network [ C]// KDD'03: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2003:137 -146.
  • 3CHEN W, WANG Y J, YANG S Y. Efficient influence maximization in social networks [ C]// KDD'09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009:199-208.
  • 4CHEN W, YUAN Y F, ZHANG L. Scalable influence maximization in social networks under the linear threshold model [C]// ICDM '10: Proceedings of the 2010 IEEE International Conference on Data Mining. Washington, DC: IEEE Computer Society, 2010: 88 - 97.
  • 5上方文Q.国内微博市场份额:新浪57%遥遥领先[EB/OL][2013-03-28].http://news.mydrivers.eom/1/197/197788htm.
  • 6RARANSJ,DAVISDK.大众传播理论:基础、争鸣与未来[M]曹书乐,译.北京:清华大学出版社,2001.
  • 7McQUAILD,WINDAHLS.大众传播模式论[M].祝建华,武伟译.上海:上海译文出版社,2000.
  • 8McQUAILD.麦奎尔大众传播理论[M].崔保国,李琨,译.4版.北京:清华大学出版社,2006.
  • 9NEWMAN M E J. Power laws, Pareto distributions and ZipFs law [ EB/OL]. [ 2013 - 03 - 28]. http://arxiv, org/pdf/cond - mat/ 0412004. pdf.
  • 10MILGRAM S. The small world problem [ J]. Psychology Today, t967, 2(1): 60-67.

同被引文献49

  • 1杜海峰,悦中山,李树茁,陈盈晖,费尔德曼.基于模块性指标的动态网络社群结构探测方法[J].系统工程理论与实践,2009,29(3):162-171. 被引量:6
  • 2孙慧英.漫谈“粉丝”现象及其文化解读[J].现代传播(中国传媒大学学报),2006,28(6):7-9. 被引量:23
  • 3郭利霞.从“粉丝”到“扇子”[J].华北电力大学学报(社会科学版),2007(3):115-118. 被引量:8
  • 4刘挺,徐志明,秦兵.从语言计算到社会计算[J].中国计算机学会通讯,2011,12(7):31-39.
  • 5Sznajd-Weron K, Sznajd J.Opinion Evolution in Closed Community[J]. International Journal of Physics C, 2000, 11(6): 1157-1165.
  • 6Deffuant G, Neau D, Amblard F, et al. Mixing Beliefs among Interacting Agents[J]. Advance in Complex System, 2000, 3(4): 87-98.
  • 7Hegeselmann R, Krause U.Opinion Dynamics Driven by Various Ways of Averaging[J]. Computational Economics, 2005, 25(4): 381-405.
  • 8Stauffer D, Sousa A O, Oliveira S M. Generalization to Square Lattice of Sznajd Sociophysics Model [J]. International Journal of Modern Physics C, 2000, 11(6): 1239-1245.
  • 9Fortunato S. On the Consensus Threshold for the Opinion Dynamics of KrauseHegselmann [J]. International Journal of Modern Physics C, 2005, 16(2):259-270.
  • 10Barabisi A L, Albert R. Emergence of Scaling in Random Networks [J]. Science, 1999, 286(5439): 509-512.

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