A Meta Learning Model for Scalable Hyperbolic Graph Neural Networks

Current research in hyperbolic neural networks (HNNs) is limited due to their lack of inductive bias mechanisms that could help them generalize over unseen tasks or enable 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 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 solves the limitation of scalability in HNNs faced by earlier 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.