Title: Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach. Abstract: Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. 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. While this approach achieves great results in the single-task setting, 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 representation learning strategy, based on meta-learning, 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 both single-task and multi-task end-to-end models. Our method is model-agnostic and task-agnostic and can hence be applied to a wide variety of multi-task domains.