Abstract: Graph convolutional networks (GCN) have recently emerged as powerful node embedding methods in network analysis tasks. Particularly, GCNs have been successfully leveraged to tackle the challenging link prediction problem, aiming at predicting missing links that exist yet were not found. However, most of these models are oriented to undirected graphs, which are limited to certain real-life applications. Therefore, based on the social ranking theory, we extend the GCN to address the directed link prediction problem. Firstly, motivated by the reciprocated and unreciprocated nature of social ties, we separate nodes in the neighbor subgraph of the missing link into the same, a higher-ranked and a lower-ranked set. Then, based on the three kinds of node sets, we propose a method to correctly aggregate and propagate the directional information across layers of a GCN model. Empirical study on 8 real-world datasets shows that our proposed method is capable of reserving rich information related to directed link direction and consistently performs well on graphs from numerous domains.
Keywords: Link prediction, graph convolutional networks, directed graphs, social ranking theory
