Network Completion via Joint Node Clustering and Similarity Learning

In this study, we investigate the problem of network completion by considering the similarities between the node attributes. Given a sample of observed nodes with their incident edges, how can we efficiently reconstruct the network by completing the missing edges of unobserved nodes? Apart from the missing edges, in real settings the node attributes may be partially missing, as well as they may introduce noise when completing the network. We propose a network completion method based on joint clustering and similarity learning. The proposed approach differs from competitive strategies, which consider attribute-based similarities at the node-level. First we generate clusters based on the node attributes, thus reducing the noise and the sparsity in the case that the attributes may be missing. We design a joint objective function to jointly factorize the adjacency matrix of the observed edges with the clusterbased similarities of the node attributes, while at the same time the clusters are adapted, accordingly. In addition, we propose an optimization algorithm to solve the network completion problem via alternating minimization. Our experiments on two real world social networks from Facebook and Google+ show that the proposed approach achieves high completion accuracy, compared to other state-of-the-art methods.