Disentangling Node Attributes from Graph Topology for Improved
  Generalizability in Link Prediction
Ayan Chatterjee Robin Walters Giulia Menichetti Tina Eliassi-Rad
Link prediction is a crucial task in graph machine learning with diverse
applications. We explore the interplay between node attributes and graph
topology and demonstrate that incorporating pre-trained node attributes
improves the generalization power of link prediction models. Our proposed
method, UPNA (Unsupervised Pre-training of Node Attributes), solves the
inductive link prediction problem by learning a function that takes a pair of
node attributes and predicts the probability of an edge, as opposed to Graph
Neural Networks (GNN), which can be prone to topological shortcuts in graphs
with power-law degree distribution. In this manner, UPNA learns a significant
part of the latent graph generation mechanism since the learned function can be
used to add incoming nodes to a growing graph. By leveraging pre-trained node
attributes, we overcome observational bias and make meaningful predictions
about unobserved nodes, surpassing state-of-the-art performance (3X to 34X
improvement on benchmark datasets). UPNA can be applied to various pairwise
learning tasks and integrated with existing link prediction models to enhance
their generalizability and bolster graph generative models.