Pairwise Learning for Neural Link Prediction

In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including \texttt{ogbl-ddi}, \texttt{ogbl-collab}, \texttt{ogbl-ppa} and \texttt{ogbl-ciation2}. PLNLP achieves Top 1 performance on \texttt{ogbl-ddi}, and Top 2 performance on \texttt{ogbl-collab} and \texttt{ogbl-ciation2} only with basic neural architecture. The performance demonstrates the effectiveness of PLNLP.