Scaling up Link Prediction with Ensembles

A network with n nodes contains O(n2 ) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Furthermore, even though link prediction is closely related to missing value estimation problems, such as collaborative filtering, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity over very large networks. Due to this computational complexity, most known link prediction methods are designed for evaluating the link propensity over a specified subset of links, rather than for performing a global search over the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this paper, we propose an ensemble enabled approach to scaling up link prediction, which is able to decompose traditional link prediction problems into subproblems of smaller size. These subproblems are each solv...

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Liang Duan, Charu Aggarwal, Shuai Ma, Renjun Hu, Jinpeng Huai
• Keywords: Data Mining, WSDM 2016