Joint Link Prediction Via Inference from a Model

A Joint Link Prediction Query (JLPQ) specifies a set of links to be predicted, given another set of links as well as node attributes as evidence. While single link prediction has been well studied in literature on deep graph learning, predicting multiple links together has gained little attention. This paper presents a novel framework for computing JLPQs using a probabilistic deep Graph Generative Model. Specifically, we develop inference procedures for an inductively trained Variational Graph Auto-Encoder (VGAE) that estimates the joint link probability for any input JLPQ, without retraining. For evaluation, we apply inference to a range of joint link prediction queries on six benchmark datasets. We find that for most datasets and query types, joint link prediction via inference from a model achieves good predictive performance, better than the independent link prediction baselines (by 0.02-0.4 AUC points depending on the dataset).