Meta-Graph: Few Shot Link Prediction via Meta Learning

Abstract

We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new graph after a small amount of training. We show that current link prediction methods are generally ill-equipped to handle this task. They cannot effectively transfer learned knowledge from one graph to another and are unable to effectively learn from sparse samples of edges. To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph. Our framework leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization. Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph can learn to quickly adapt to a new graph using only a small sample of true edges, enabling not only fast adaptation but also improved results at convergence.

References in corpus (6)

• Semi-Supervised Classification with Graph Convolutional Networks
• Theoretical Models of Learning to Learn
• Variational Graph Auto-Encoders
• Predicting multicellular function through multi-layer tissue networks
• Learning to Remember Rare Events
• Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

Cited by in corpus (5)

• Trans4E: Link Prediction on Scholarly Knowledge Graphs
• A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
• Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
• Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
• What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data