  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.
