  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, the 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.
