  Hypergraphs serve as an effective model for depicting complex connections in
various real-world scenarios, from social to biological networks. The
development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable
method to manage the intricate associations in data, though scalability is a
notable challenge due to memory limitations. In this study, we introduce a new
adaptive sampling strategy specifically designed for hypergraphs, which tackles
their unique complexities in an efficient manner. We also present a Random
Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron
(MLP) module to improve the robustness and generalization capabilities of our
approach. Thorough experiments with real-world datasets have proven the
effectiveness of our method, markedly reducing computational and memory demands
while maintaining performance levels akin to conventional HGNNs and other
baseline models. This research paves the way for improving both the scalability
and efficacy of HGNNs in extensive applications. We will also make our codebase
publicly accessible.
