Transitive node similarity for link prediction in social networks with positive and negative links

Authors:
Panagiotis Symeonidis;Eleftherios Tiakas;Yannis Manolopoulos
Affiliations:
Aristotle University of Thessaloniki, Thessaloniki, Greece;Aristotle University of Thessaloniki, Thessaloniki, Greece;Aristotle University of Thessaloniki, Thessaloniki, Greece
Venue:
Proceedings of the fourth ACM conference on Recommender systems
Year:
2010

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Abstract

Online social networks (OSNs) like Facebook, and Myspace recommend new friends to registered users based on local features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features. We also exploit global graph features introducing transitive node similarity. Moreover, we derive variants of our method that apply in signed networks. We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. We show that a significant accuracy improvement can be gained by using information about both positive and negative edges.