Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach

• URL: http://arxiv.org/abs/2201.03326v1
• Date: Mon, 10 Jan 2022 12:58:46 GMT
• Title: Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
• Authors: Davide Buffelli, Fabio Vandin
• Abstract summary: We propose a novel training strategy for graph representation learning, based on meta-learning. Our method avoids the difficulties arising when learning to perform multiple tasks concurrently. We show that the embeddings produced by a model trained with our method can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.
• Score: 5.629161809575013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 still an open problem. We propose a novel training strategy for graph representation learning, based on meta-learning, which allows the training of a GNN model 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 a model trained with our method can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.

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