  The progress in hyperbolic neural networks (HNNs) research is hindered by
their absence of inductive bias mechanisms, which are essential for
generalizing to new tasks and facilitating scalable learning over large
datasets. In this paper, we aim to alleviate these issues by learning
generalizable inductive biases from the nodes' local subgraph and transfer them
for faster learning over new subgraphs with a disjoint set of nodes, edges, and
labels in a few-shot setting. We introduce a novel method, Hyperbolic GRAph
Meta Learner (H-GRAM), that, for the tasks of node classification and link
prediction, learns transferable information from a set of support local
subgraphs in the form of hyperbolic meta gradients and label hyperbolic
protonets to enable faster learning over a query set of new tasks dealing with
disjoint subgraphs. Furthermore, we show that an extension of our meta-learning
framework also mitigates the scalability challenges seen in HNNs faced by
existing approaches. Our comparative analysis shows that H-GRAM effectively
learns and transfers information in multiple challenging few-shot settings
compared to other state-of-the-art baselines. Additionally, we demonstrate
that, unlike standard HNNs, our approach is able to scale over large graph
datasets and improve performance over its Euclidean counterparts.
