期刊
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
卷 7, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2499907.2499908
关键词
Social network; reciprocal relationship; social influence; predictive model; link prediction; Twitter
资金
- National Basic Research Program of China [2011CBA00300, 2011CBA00301, 2011CB302302]
- National Natural Science Foundation of China [61033001, 61061130540, 61073174]
- Natural Science Foundation of China [61073073, 612222212]
- Chinese National Key Foundation Research [60933013, 61035004]
- U.S. Air Force Office of Scientific Research [FA9550-09-1-0675]
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.
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