  Graph Neural Networks (GNNs) are a framework for graph representation
learning, where a model learns to generate low dimensional node embeddings that
encapsulate structural and feature-related information. GNNs are usually
trained in an end-to-end fashion, leading to highly specialized node
embeddings. However, generating node embeddings that can be used to perform
multiple tasks (with performance comparable to single-task models) is an open
problem. We propose a novel meta-learning strategy capable of producing
multi-task node embeddings. Our method avoids the difficulties arising when
learning to perform multiple tasks concurrently by, instead, learning to
quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks
singularly. We show that the embeddings produced by our method can be used to
perform multiple tasks with comparable or higher performance than classically
trained models. Our method is model-agnostic and task-agnostic, thus applicable
to a wide variety of multi-task domains.
