Learning to Predict Reciprocity and Triadic Closure in Social Networks

被引:83
作者
Lou, Tiancheng [1 ]
Tang, Jie [2 ]
Hopcroft, John [3 ]
Fang, Zhanpeng [2 ]
Ding, Xiaowen [1 ]
机构
[1] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing, Peoples R China
[2] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China
[3] Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA
基金
中国国家自然科学基金;
关键词
Social network; reciprocal relationship; social influence; predictive model; link prediction; Twitter;
D O I
10.1145/2499907.2499908
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We study how links are formed in social networks. In particular, we focus on investigating how a reciprocal (two-way) link, the basic relationship in social networks, is developed from a parasocial (one-way) relationship and how the relationships further develop into triadic closure, one of the fundamental processes of link formation. We first investigate how geographic distance and interactions between users influence the formation of link structure among users. Then we study how social theories including homophily, social balance, and social status are satisfied over networks with parasocial and reciprocal relationships. The study unveils several interesting phenomena. For example, "friend's friend is a friend" indeed exists in the reciprocal relationship network, but does not hold in the parasocial relationship network. We propose a learning framework to formulate the problems of predicting reciprocity and triadic closure into a graphical model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a Twitter network. The proposed model also achieves better performance (+20-30% in terms of F1-measure) than several alternative methods for predicting the triadic closure formation.
引用
收藏
页数:25
相关论文
共 40 条
[1]  
[Anonymous], 1999, SIDLWP19990120 STANF
[2]  
[Anonymous], 2009, PNAS
[3]  
[Anonymous], 2010, P INT C WORLD WID WE
[4]  
[Anonymous], 2011, PROC 4 ACM INT C WEB, DOI DOI 10.1145/1935826.1935914
[5]  
[Anonymous], P AAAI INT C WEBL SO
[6]  
[Anonymous], 2010, Proceedings of the 2010 international conference on Management of data
[7]  
[Anonymous], 2010, Networks, crowds, and markets
[8]  
[Anonymous], 2010, P 19 ACM INT C INF K, DOI DOI 10.1145/1871437.1871691
[9]  
[Anonymous], 2007, P 22 NAT C ART INT
[10]  
[Anonymous], 2010, P 3 ACM INT C WEB SE, DOI DOI 10.1145/1718487.1718520