  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 ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top
1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on
ogbl-ciation2 only with basic neural architecture. The performance demonstrates
the effectiveness of PLNLP.
