Generalization Performance of Hypergraph Neural Networks

TL;DR

This paper develops theoretical generalization bounds for hypergraph neural networks using the PAC-Bayes framework, linking hypergraph structure and spectral norms to model performance, supported by empirical validation.

Contribution

It introduces margin-based generalization bounds for four classes of hypergraph neural networks, providing new insights into their theoretical performance guarantees.

Findings

Strong correlation between theoretical bounds and empirical loss.

Hypergraph structure and spectral norms significantly influence generalization.

Empirical results confirm the bounds' predictive power.

Abstract

Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us. In this paper, we seek to develop margin-based generalization bounds for four representative classes of hypergraph neural networks, including convolutional-based methods (UniGCN), set-based aggregation (AllDeepSets), invariant and equivariant transformations (M-IGN), and tensor-based approaches (T-MPHN). Through the PAC-Bayes framework, our results reveal the manner in which hypergraph structure and spectral norms of the learned weights can affect the generalization bounds, where the key technical challenge lies in developing new perturbation analysis for hypergraph neural networks, which offers a rigorous understanding of how variations in the model's weights and hypergraph structure impact its generalization behavior. Our empirical study examines the relationship between the practical performance and theoretical bounds of the models over synthetic and real-world datasets. One of our primary observations is the strong correlation between the theoretical bounds and empirical loss, with statistically significant consistency in most cases.