[Objective] Study on the effects of different triadic structures on follow influence in relation formation. [Methods] This paper uses questionnaires on 221 students at different time to get the dynamic evolution process of this network, and then analyzes the effects of different triadic structures on relation formation. [Results] The results show that triadic structures with reciprocity, transitivity and revesed relationship are more likely to form a new relation. [Limitations] This paper is unable to completely control the influences besides relation network. [Conclusions] The pattern of online and offline relation formation is the same, which is valuable for bussiness.
吴江, 张劲帆. 社会网络三元结构中关注影响力研究——以学生关系网络为例[J]. 现代图书情报技术, 2015, 31(10): 72-80.
Wu Jiang, Zhang Jinfan. Research on Follow Influence of Triadic Structure in Social Network——Take Student Relation Network as an Example. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.10.10.
[1] Wasserman S, Faust K. Social Network Analysis: Methods and Applications [M]. Cambridge University Press, 1994: 25.
[2] Milo R, Shen-Orr S, Itzkovitz S, et al. Network Motifs: Simple Building Blocks of Complex Networks [J]. Science, 2002, 298(5594): 824-827.
[3] Donald H, Wohl R R. Mass Communication and Parasocial Interaction [J]. Psychiatry, 1956, 19(3): 215-229.
[4] Romero D M, Kleinberg J. The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter [C]. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 2010: 138-145.
[5] Tang J, Sun J, Wang C, et al. Social Influence Analysis in Large-scale Networks [C]. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009: 807-816.
[6] Tang J, Wu S, Sun J, et al. Cross-domain Collaboration Recommendation [C]. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2012: 1285-1293.
[7] Tan C, Lee L, Tang J, et al. User-level Sentiment Analysis Incorporating Social Networks [C]. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011: 1397-1405.
[8] Sun J, Tang J. Models and Algorithms for Social Influence Analysis [C]. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 2013: 775-776.
[9] Heider F. The Psychology of Interpersonal Relations [M]. Psychology Press, 1982.
[10] Klimek P, Thurner S. Triadic Closure Dynamics Drives Scaling Laws in Social Multiplex Networks [J]. New Journal of Physics, 2013, 15(6): Article No.063008.
[11] Li M, Zou H, Guan S, et al. A Coevolving Model Based on Preferential Triadic Closure for Social Media Networks [J]. Scientific Reports, 2013, 3: Article No. 2512.
[12] Lou T, Tang J, Hopcroft J, et al. Learning to Predict Reciprocity and Triadic Closure in Social Networks [J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2013, 7(2): Article No. 5.
[13] Mollenhorst G, Völker B, Flap H. Shared Contexts and Triadic Closure in Core Discussion Networks [J]. Social Networks, 2011, 33(4): 292-302.
[14] Kossinets G, Watts D J. Empirical Analysis of an Evolving Social Network [J]. Science, 2006, 311(5757): 88-90.
[15] Hopcroft J, Lou T, Tang J. Who will Follow You Back?: Reciprocal Relationship Prediction[C]. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 2011: 1137-1146.
[16] Seshadhri C, Pinar A, Durak N, et al. Directed Closure Measures for Networks with Reciprocity [OL]. arXiv Preprint, 2013. arXiv: 1302.6220.
[17] Snijders T A B, Pattison P E, Robins G L, et al. New Specifications for Exponential Random Graph Models [J]. Sociological Methodology, 2006, 36(1): 99-153.
[18] Robins G, Snijders T, Wang P, et al. Recent Developments in Exponential Random Graph (p*) Models for Social Networks [J]. Social Networks, 2007, 29(2): 192-215.
[19] Kwak H, Lee C, Park H, et al. What is Twitter, a Social Network or a News Media? [C]. In: Proceedings of the 19th International Conference on World Wide Web. ACM, 2010: 591-600.
[20] Gouldner A W. The Norm of Reciprocity: A Preliminary Statement [J]. Journal of Social and Personal Relationships, 1960, 25(2): 161-178.
[21] Watts D J, Strogatz S H. Collective Dynamics of ‘Small-World' Networks [J]. Nature, 1998, 393(6684): 440-442.
[22] McPherson M, Smith-Lovin L, Cook J M. Birds of a Feather: Homophily in Social Networks [J]. Annual Review of Sociology, 2001, 27: 415-444.
[23] Huang H, Tang J, Wu S, et al. Mining Triadic Closure Patterns in Social Networks[C]. In: Proceedings of the 23rd International Conference on World Wide. International World Wide Web Conferences Steering Committee, 2014: 499-504.
[24] Easley D, Kleinberg J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World [M]. Cambridge University Press, 2010.Burt R S. Structural Holes: The Social Structure of Competition [M]. Harvard University Press, 2009.
