  We propose Unsupervised Episode Generation method called Neighbors as Queries
(NaQ) to solve the Few-Shot Node-Classification (FSNC) task by unsupervised
Graph Meta-learning. Doing so enables full utilization of the information of
all nodes in a graph, which is not possible in current supervised meta-learning
methods for FSNC due to the label-scarcity problem. In addition, unlike
unsupervised Graph Contrastive Learning (GCL) methods that overlook the
downstream task to be solved at the training phase resulting in vulnerability
to class imbalance of a graph, we adopt the episodic learning framework that
allows the model to be aware of the downstream task format, i.e., FSNC. The
proposed NaQ is a simple but effective unsupervised episode generation method
that randomly samples nodes from a graph to make a support set, followed by
similarity-based sampling of nodes to make the corresponding query set. Since
NaQ is model-agnostic, any existing supervised graph meta-learning methods can
be trained in an unsupervised manner, while not sacrificing much of their
performance or sometimes even improving them. Extensive experimental results
demonstrate the effectiveness of our proposed unsupervised episode generation
method for graph meta-learning towards the FSNC task. Our code is available at:
https://github.com/JhngJng/NaQ-PyTorch.
