Pairwise Learning for Neural Link Prediction. (arXiv:2112.02936v6 [cs.LG] UPDATED)

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 …

arxiv learning link prediction prediction