Link Prediction via Higher-Order Motif Features

Link prediction requires predicting which new links are likely to appear in a graph. In this paper, we present an approach for link prediction that relies on higher-order analysis of the graph topology, well beyond the typical approach which relies on common neighbors. We treat the link prediction problem as a supervised classification problem, and we propose a set of features that depend on the patterns or motifs that a pair of nodes occurs in. By using motifs of sizes 3, 4, and 5, our approach captures a high level of detail about the graph topology. In addition, we propose two optimizations to construct the classification dataset from the graph. First, we propose adding negative examples to the graph as an alternative to the common approach of removing positive ones. Second, we show that it is important to control for the shortest-path distance when sampling pairs of nodes to form negative examples, since the difficulty of prediction varies with the distance. We experimentally demonstrate that using our proposed motif features in off-the-shelf classifiers results in up to 10% points increase in accuracy over prior topology-based and feature-learning methods.