论文标题
多任务设置中的图形表示学习的元学习方法
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
论文作者
论文摘要
图神经网络（GNN）是图表学习的框架，其中模型学会生成封装结构和特征相关信息的低维节点嵌入。 GNN通常以端到端的方式进行训练，从而导致高度专业的节点嵌入。但是，生成可用于执行多个任务的节点嵌入（具有与单任务模型相当的性能）是一个开放的问题。我们提出了一种能够产生多任务节点嵌入的新型元学习策略。我们的方法避免了学习在学习执行多个任务时同时学习快速（即有几步梯度下降）时出现的困难。我们表明，与经典训练的模型相比，我们方法生产的嵌入方式可用于执行具有可比或更高性能的多个任务。我们的方法是模型不合时宜的和任务敏捷的方法，因此适用于各种多任务域。
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.
