Attributed Network Representation Learning Approaches for Link Prediction

Network representation learning algorithms seek to embed the nodes of a network into a lower-dimensional feature space such that nodes that are in close proximity to each other share a similar representation. In this paper, we investigate the effectiveness of using network representation learning algorithms for link prediction problems. Specifically, we demonstrate the limitations of existing algorithms in terms of their ability to accurately predict links between nodes that are in the same or different communities and nodes that have low degrees. We also show that incorporating node attribute information can help alleviate this problem and compare three different approaches to integrate this information with network representation learning for link prediction problems. Using five real-world network datasets, we demonstrate the efficacy of one such approach, called SPIN, that can effectively combine the link structure with node attribute information and predict links between nodes in the same and different communities without favoring high degree nodes.