Meta-Learning with Graph Neural Networks: Methods and Applications
It addresses the challenge of few-sample scenarios in graph data for researchers and practitioners, but it is incremental as it reviews existing methods rather than introducing new ones.
This paper surveys meta-learning approaches combined with Graph Neural Networks (GNNs) to tackle the problem of limited samples in graph-based applications, highlighting their effectiveness across various domains.
Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different meta-learning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.