理解社交网络上的信息传播机制,通常包括对拓扑结构的分析和对用户行为的分析。由于社交网络上连边的强度具有异质性,只有一部分连边对于信息传播有实质作用,构成隐藏在社交网络中的影响力骨架。对影响力骨架的拓扑研究可帮助我们获得比直接研究社交网络拓扑结构更深入的认识。我们从连边正负性和个体节点角色分化入手,探讨了微观层面连边和节点在信息传播中的作用,进而从宏观层面分析信息传播所依赖的影响力骨架的连通性和扩散效率,发现信息传播具有一定程度的脆弱性,且其传播效率低于对社交网络本身研究的预期。
Abstract
Understanding intrinsic mechanism of information propagations on social networks has attracted growing attention, including social network topology analysis and user behavior analysis. Due to the heterogeneity of links in social networks, only a portion of links significantly contribute to information propagations. The influence backbone of a social network, consisting of those links, might provide deeper insight to information propagations. Focused on the influence backbone, we analyzes the signs of links with social structural balance theory, and the roles of nodes with heterogeneous distributions of out-degrees, so as to find the roles played by links and nodes in information propagations in a microscopic. Furthermore, we investigate the network connectivity and information spread efficiency of the influence backbone, finding that information propagations are more fragile and less effective.
关键词
信息传播 /
社交网络 /
影响力骨架
{{custom_keyword}} /
Key words
information propagations /
social networks /
influence backbone
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Robert M Bond,et al. A 61-million-person experiment in social influence and political mobilization [J]. Nature, 2012, 489(7415): 295-298.
[2] Johan Bollen, Huina Mao. Twitter Mood as a Stock Market Predictor [J]. Computer, 2011, 44(10): 91-94.
[3] David Kempe, Jon Kleinberg, Eva Tardos. Maximizing the spread of influence through a social network [C]//Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining. Washington, D.C. 2003.
[4] Jeremy Ginsberg,et al. Detecting influenza epidemics using search engine query data [J]. Nature, 2009. 457(7232): 1012-1014.
[5] Jimeng Sun, Jie Tang. A Survey of Models and Algorithms for Social Influence Analysis [M]. Social Network Data Analytics. C. C. Aggarwal, Springer US, 2011: 177-214.
[6] Lü Linyuan, Chen Duan-Bing, Zhou Tao. The small world yields the most effective information spreading [J]. New Journal of Physics, 2011, 13(12): 123005.
[7] Lü Linyuan,et al. Leaders in Social Networks, the Delicious Case [J]. PLoS ONE, 2011, 6(6): e21202.
[8] Jie Tang, Tiancheng Lou, Jon Kleinberg. Inferring social ties across heterogenous networks [C]//Proceedings of the 5th ACM international conference on Web search and data mining. Seattle, Washington, USA, ACM, 2012: 743-752.
[9] Johan Ugander,et al. Structural diversity in social contagion [J]. Proceedings of the National Academy of Sciences, 2012, 109(16): 5962-5966.
[10] Peng Bao,et al. Popularity prediction in microblogging network: a case study on sina weibo [C]//Proceedings of the 22nd international conference on World Wide Web companion, Rio de Janeiro, Brazil, 2013
[11] Kazumi Saito,Ryohei Nakano, Masahiro Kimura. Prediction of Information Diffusion Probabilities for Independent Cascade Model [C]//Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems. Springer Berlin Heidelberg. 2008: 67-75.
[12] Amit Goyal, Francesco Bonchi, Laks V S Lakshmanan. Learning influence probabilities in social networks [C]//Proceedings of the 3rd ACM international conference on Web search and data mining, New York, USA, ACM, 2010.
[13] Manuel Gomez-Rodriguez, Jure Leskovec, Andreas Krause. Inferring Networks of Diffusion and Influence [J]. ACM Trans. Knowl. Discov. Data, 2012, 5(4): 1-37.
[14] Manuel Gomez-Rodriguez, et al. Structure and dynamics of information pathways in online media [C]//Proceedings of the 6th ACM international conference on Web search and data mining, Rome, Italy, ACM, 2013.
[15] Paula Fitzgerald Bone. Word-of-mouth effects on short-term and long-term product judgments [J]. Journal of Business Research, 1995, 32(3): 213-223.
[16] Sinan Aral, Dylan Walker. Identifying influential and susceptible members of social networks [J]. Science, 2012, 337(6092):337-341.
[17] Fang Wu, Bernardo A. Huberman. Novelty and collective attention [J]. Proceedings of the National Academy of Sciences, 2007, 104(45): 17599-17601.
[18] MeredithRingel Morris, et al. Tweeting is believing? understanding microblog credibility perceptions [C]//Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work. Seattle, Washington, USA, ACM, 2012.
[19] Mark SGranovetter. The strength of weak ties [J]. American journal of sociology, 1973, 78(6): 1360-1380.
[20] Junming Huang, et al. Exploring social influence via posterior effect of word-of-mouth recommendations [C]//Proceedings of the 5th ACM international conference on Web search and data mining. Seattle, Washington, USA, ACM, 2012.
[21] Ruslan Salakhutdinov, Andriy Mnih. Probabilistic Matrix Factorization [J]. Advances in Neural Information Processing Systems 20. Cambridge, MA, MIT Press, 2008: 1257-1264.
[22] Heider F. Attitudes and cognitive organization [J]. Journal of Psychology, 1946, 21(1):107-112.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家基础研究发展计划(973)(2012CB316303,2013CB329602);国家自然科学基金(61232010,61202215);北京市自然科学基金(4122077)
{{custom_fund}}
PDF(2882 KB)
