  Attributed networks nowadays are ubiquitous in a myriad of high-impact
applications, such as social network analysis, financial fraud detection, and
drug discovery. As a central analytical task on attributed networks, node
classification has received much attention in the research community. In
real-world attributed networks, a large portion of node classes only contain
limited labeled instances, rendering a long-tail node class distribution.
Existing node classification algorithms are unequipped to handle the
\textit{few-shot} node classes. As a remedy, few-shot learning has attracted a
surge of attention in the research community. Yet, few-shot node classification
remains a challenging problem as we need to address the following questions:
(i) How to extract meta-knowledge from an attributed network for few-shot node
classification? (ii) How to identify the informativeness of each labeled
instance for building a robust and effective model? To answer these questions,
in this paper, we propose a graph meta-learning framework -- Graph Prototypical
Networks (GPN). By constructing a pool of semi-supervised node classification
tasks to mimic the real test environment, GPN is able to perform
\textit{meta-learning} on an attributed network and derive a highly
generalizable model for handling the target classification task. Extensive
experiments demonstrate the superior capability of GPN in few-shot node
classification.
