Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning

Abstract

Given an edge-incomplete graph, how can we accurately find its missing links? The problem aims to discover the missing relations between entities when their relationships are represented as a graph. Edge-incomplete graphs are prevalent in real-world due to practical limitations, such as not checking all users when adding friends in a social network. Addressing the problem is crucial for various tasks, including recommending friends in social networks and finding references in citation networks. However, previous approaches rely heavily on the given edge-incomplete (observed) graph, making it challenging to consider the missing (unobserved) links. In this paper, we propose PULL, an accurate link prediction method based on the positive-unlabeled (PU) learning. PULL treats the observed edges in the training graph as positive examples, and the unconnected node pairs as unlabeled ones. PULL effectively prevents the link predictor from blindly trusting the observed graph by proposing latent variables for every edge, and leveraging the expected graph structure with respect to these variables. Extensive experiments on real- world datasets show that PULL consistently outperforms the baselines for predicting links in edge-incomplete graphs.