In this paper, we investigate Unsupervised Episode Generation methods to
solve Few-Shot Node-Classification (FSNC) problem via Meta-learning without
labels. Dominant meta-learning methodologies for FSNC were developed under the
existence of abundant labeled nodes for training, which however may not be
possible to obtain in the real-world. Although few studies have been proposed
to tackle the label-scarcity problem, they still rely on a limited amount of
labeled data, which hinders the full utilization of the information of all
nodes in a graph. Despite the effectiveness of Self-Supervised Learning (SSL)
approaches on FSNC without labels, they mainly learn generic node embeddings
without consideration on the downstream task to be solved, which may limit its
performance. In this work, we propose unsupervised episode generation methods
to benefit from their generalization ability for FSNC tasks while resolving
label-scarcity problem. We first propose a method that utilizes graph
augmentation to generate training episodes called g-UMTRA, which however has
several drawbacks, i.e., 1) increased training time due to the computation of
augmented features and 2) low applicability to existing baselines. Hence, we
propose Neighbors as Queries (NaQ), which generates episodes from structural
neighbors found by graph diffusion. Our proposed methods are model-agnostic,
that is, they can be plugged into any existing graph meta-learning models,
while not sacrificing much of their performance or sometimes even improving
them. We provide theoretical insights to support why our unsupervised episode
generation methodologies work, and extensive experimental results demonstrate
the potential of our unsupervised episode generation methods for graph
meta-learning towards FSNC problems.Comment: 11 pages, 9 figures, preprin