TIGP (BIO)—Link prediction via exploring common neighborhoods

Abstract

Social network analysis aims to establish properties of a network by exploring link structure of the network. However, due to concerns such as confidential and privacy, a social network may not provide full information on its links. As some of the links are missing, it is difficult to establish the network's properties by exploring its link structure. In this paper we propose a method for recovering such missing links. We pay attention on a situation in which some nodes have fully-observed links. The method relies on exploiting the network of these anchor nodes to recover missing links of nodes that have neighborhoods overlapping with the anchor nodes. It uses a graph neural network to extract information from these neighborhoods, and then applies the information to a regression model for missing link recovery. We demonstrated this method on real-world social network data. The results show that this method can achieve better performance than traditional methods that are solely based on node attributes for missing link recovery.

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