  Graphs are widely used to model the relational structure of data, and the
research of graph machine learning (ML) has a wide spectrum of applications
ranging from drug design in molecular graphs to friendship recommendation in
social networks. Prevailing approaches for graph ML typically require abundant
labeled instances in achieving satisfactory results, which is commonly
infeasible in real-world scenarios since labeled data for newly emerged
concepts (e.g., new categorizations of nodes) on graphs is limited. Though
meta-learning has been applied to different few-shot graph learning problems,
most existing efforts predominately assume that all the data from those seen
classes is gold-labeled, while those methods may lose their efficacy when the
seen data is weakly-labeled with severe label noise. As such, we aim to
investigate a novel problem of weakly-supervised graph meta-learning for
improving the model robustness in terms of knowledge transfer. To achieve this
goal, we propose a new graph meta-learning framework -- Graph Hallucination
Networks (Meta-GHN) in this paper. Based on a new robustness-enhanced episodic
training, Meta-GHN is meta-learned to hallucinate clean node representations
from weakly-labeled data and extracts highly transferable meta-knowledge, which
enables the model to quickly adapt to unseen tasks with few labeled instances.
Extensive experiments demonstrate the superiority of Meta-GHN over existing
graph meta-learning studies on the task of weakly-supervised few-shot node
classification.
