Link Prediction for Bipartite Social Networks: The Role of Structural Holes

Authors:
Shuang Xia;BingTian Dai;Ee-Peng Lim;Yong Zhang;Chunxiao Xing
Affiliations:
-;-;-;-;-
Venue:
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year:
2012

Citing 6
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Abstract

Link prediction is an important problem in social network mining. Traditional neighborhood based methods such as Common neighbors, Jaccard Coefficient and Adamic/Adar are well studied in link prediction. However, the concept of structural holes does not receive significant attention in link prediction. As a preliminary work in studying structural holes, we focus on bipartite social networks, which is a special class of social networks that consists of two distinct roles for the users, and links are between users of different roles. In this study, a few implementations of structural holes are proposed, which are then validated with extended neighborhood based methods on a real dataset derived from IMDb network. The results show that structural holes help in improving accuracies in link prediction.