Link Prediction with Contextualized Self-Supervision

Link prediction aims to infer the existence of a link between two nodes in a network. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges -- link sparsity, node attribute noise and network dynamics -- that are faced by real-world networks. To overcome these challenges, we propose a Contextualized Self-Supervised Learning (CSSL) framework that fully exploits structural context prediction for link prediction. The proposed CSSL framework forms edge embeddings through aggregating pairs of node embeddings constructed via a transformation on node attributes, which are used to predict the link existence probability. To generate node embeddings tailored for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost link prediction. Two types of structural contexts are investigated, i.e., context nodes collected from random walks vs. context subgraphs. The CSSL framework can be trained in an end-to-end manner, with the learning of node and edge embeddings supervised by link prediction and the self-supervised learning task. The proposed CSSL is a generic and flexible framework in the sense that it can handle both transductive and inductive link prediction settings, and both attributed and non-attributed networks. Extensive experiments and ablation studies on seven real-world benchmark graph datasets demonstrate the superior performance of the proposed self-supervision based link prediction algorithm over state-of-the-art baselines on different types of networks under both transductive and inductive settings. The proposed CSSL also yields competitive performance in terms of its robustness to node attribute noise and scalability over large-scale networks.