Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

TL;DR

This paper introduces Graph Extrapolation Networks (GEN), a transductive meta-learning framework designed to improve out-of-graph link prediction in evolving multi-relational graphs, especially for unseen nodes with few links.

Contribution

The paper proposes a novel meta-learning approach that jointly learns inductive and transductive inference models for out-of-graph link prediction, including a stochastic embedding layer to handle uncertainty.

Findings

GEN significantly outperforms baselines on knowledge graph completion tasks.

The model effectively predicts links involving unseen nodes with few edges.

Results demonstrate improved accuracy in drug-drug interaction prediction.

Abstract

Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction task but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.