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Learning to predict reciprocity and triadic closure in social networks

Learning to predict reciprocity and triadic closure in social networks Learning to Predict Reciprocity and Triadic Closure in Social Networks TIANCHENG LOU and JIE TANG, Tsinghua University JOHN HOPCROFT, Cornell University ZHANPENG FANG and XIAOWEN DING, Tsinghua University 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 (+20­30% in terms of F1-measure) than several alternative http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Learning to predict reciprocity and triadic closure in social networks

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/2499907.2499908
Publisher site
See Article on Publisher Site

Abstract

Learning to Predict Reciprocity and Triadic Closure in Social Networks TIANCHENG LOU and JIE TANG, Tsinghua University JOHN HOPCROFT, Cornell University ZHANPENG FANG and XIAOWEN DING, Tsinghua University 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 (+20­30% in terms of F1-measure) than several alternative

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Jul 1, 2013

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