  Link prediction aims to infer the link existence between pairs of nodes in
networks/graphs. Despite their wide application, the success of traditional
link prediction algorithms is hindered by three major challenges -- link
sparsity, node attribute noise and dynamic changes -- that are faced by many
real-world networks. To address these challenges, we propose a Contextualized
Self-Supervised Learning (CSSL) framework that fully exploits structural
context prediction for link prediction. The proposed CSSL framework learns a
link encoder to infer the link existence probability from paired node
embeddings, which are constructed via a transformation on node attributes. To
generate informative node embeddings for link prediction, structural context
prediction is leveraged as a self-supervised learning task to boost the link
prediction performance. Two types of structural context 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 model
parameters supervised by both the link prediction and self-supervised learning
tasks. The proposed CSSL is a generic and flexible framework in the sense that
it can handle both attributed and non-attributed networks, and operate under
both transductive and inductive link prediction settings. Extensive experiments
and ablation studies on seven real-world benchmark networks 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.

Social and Information Networks;Artificial Intelligence