A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
This work is significant for researchers and practitioners working with GNNs in multi-task settings, offering a way to generate more versatile node embeddings without sacrificing performance.
This paper addresses the challenge of creating multi-task node embeddings using Graph Neural Networks (GNNs). The authors propose a meta-learning strategy that enables GNNs to quickly adapt to multiple tasks individually, producing embeddings that achieve comparable or superior performance to single-task models.
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.