考虑社交网络初始阶段的演化过程对于定量认识和理解人际关系的形成与演化的重要意义,搜集“冰桶挑战”事件国内从事件发起到第6天的数据。以挑战者为节点,点名关系为边构造社交网络。通过分析该网络的统计指标,发现网络密度一直减小;网络效率先减小,后又缓慢增加;连通子图的数量先迅速增加,最高增加了初始值的5倍,后又减小;网络效率与子图数量呈负相关关系。考虑到该网络构建的特殊性,与其他社交网络的演化做了对比分析。
杨凯
,
刘晓露
,
林坚洪
,
成曦
,
郭强
,
刘建国
,
杨凯
,
刘晓露
,
林坚洪
,
成曦
,
郭强
,
刘建国
. “冰桶挑战”诱导的社交网络演化分析[J]. 复杂系统与复杂性科学, 2016
, 13(2)
: 90
-96
.
DOI: 10.13306/j.1672-3813.2016.02.011
The evolutionary process of social networks at initial period is very important, especially for the quantitative understanding of the formation and the evolution of interpersonal relationships. In this paper, combining with the “Ice Bucket Challenge”, we collect the data of this event from the launch to the sixth day in our country. The nodes stand for the challengers and the edges are the relations of called people in the social networks. By analyzing the rules of the structural characteristics, including the network size, the clustering coefficient, density, network efficiency and connectivity sub-graphs, we find that the clustering coefficient increased from zero to 0.0167 at the beginning and then decreases; the densityof the network declines from 0.1209 over time; the network efficiency reduces by 81.4% at first and then slowly increases; the connected sub-graphs rapidly increases five times and then decreases; the network efficiency and the number of sub-graphs are negatively correlative. Taking into account the specificity of the network,we compare with evolution of other social networks.Thiswork will be helpful for understanding the law of the formation and development of the early social networks.
[1] 斯坦利·沃瑟曼,凯瑟琳·福斯特. 社会网络分析: 方法与应用[M].陈禹,孙彩虹,译. 北京: 中国人民大学出版社, 2012.
[2] Dodds P S, Muhamad R, Watts D J. An experimental study of search in global social networks[J].Science, 2003, 301(5634): 827-829.
[3] Shin D H. The effects of trust, security and privacy in social networking: a security-based approach to understand the pattern of adoption[J].Interacting with Computers, 2010, 22(5): 428-438.
[4] MisloveA, Marcon M, Gummadi K P, et al. Measurement and analysis of online social networks[C]//Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. ACM, 2007: 29-42.
[5] Hellmann T, Staudigl M. Evolution of social networks[J].European Journal of Operational Research, 2014, 234(3): 583-596.
[6] Watts D J, Strogatz S H. Collective dynamics of ‘small-world’networks[J].Nature, 1998, 393(6684): 440-442.
[7] Newman M E J, Watts D J. Renormalization group analysis of the small-world network model[J].Physics Letters A, 1999, 263(4): 341-346.
[8] Barabási A L, Albert R. Emergence of scaling in random networks[J].Science, 1999, 286(5439): 509-512.
[9] 张立, 刘云. 虚拟社区网络的演化过程研究[J].物理学报, 2008, 57(9): 5419-5424.
Zhang L, Liu Y. Research on the evolution process of virtual community networks[J].Acta Physica Sinica, 2008, 57(9): 5419-5424.
[10] 熊熙,曹伟, 周欣, 等. 社交网络形成和演化的特征模型研究[J].四川大学学报: 工程科学版, 2012, 44(004): 140-144.
Xiong X, Cao W, Zhou X, etal. Research on the feature model of the formation and evolution of social networks[J].Journal of SichuanUniversity(Engineering Science Edition), 2012, 44(004): 140-144.
[11] Kumar R, Novak J, Tomkins A. Structure and evolution of online social networks[M]//Link Mining: Models, Algorithms, and Applications. Springer New York, 2010: 337-357.
[12] 郭海霞. 新型社交网络信息传播特点和模型分析[J].现代情报, 2012, 32(1): 56-59.
GuoHaixia. New Social networking features andmodel analysis of information dissemination[J].Journal of Modern Information, 2012, 32(1): 56-59.
[13] 汪小帆,李翔,陈关荣.网络科学导论[M].北京:高等教育出版社,2012.
[14] Viswanath B, Mislove A, Cha M, et al. On the evolution of user interaction in Facebook[C]//Proceedings of the 2nd ACM Workshop on Online Social Networks. ACM, 2009: 37-42.
[15] Latora V, Marchiori M. Efficient behavior of small-world networks[J].Physical Review Letters, 2001, 87(19): 198701.