A Novel Strategy for Link Prediction in Social Networks

Abstract

The problem of link prediction has gained a lot of attention recently from the research community. It can be formalized as, given a snapshot of a social network at time t, can it be predicted which new connections among its members are likely to occur in the future at time t'. Apart from analysing social networks, it has also found application in other domains e.g., information retrieval, bio-informatics and e-commerce. Topological information of the network, i.e. the information about the present nodes and links, can be used to predict the future links in the network. As an example, "Common neighbors" method is a trivial but efficient strategy for predicting the possibility of a link between a pair of nodes. Many variants of the common neighbors method have been proposed to address this problem. In this paper, we propose a novel strategy for predicting the missing links which also takes into account the number of links between two sets of uncommon neighbors of given nodes, in addition to their common neighbors.