Residual Enhanced Multi-Hypergraph Neural Network

TL;DR

This paper introduces a Residual Enhanced Multi-Hypergraph Neural Network that effectively fuses multi-modal hypergraph data and overcomes over-smoothing, achieving state-of-the-art results on 3D benchmarks.

Contribution

It proposes a novel residual multi-hypergraph neural network architecture that enhances multi-modal data fusion and mitigates over-smoothing in hypergraph learning.

Findings

Achieves state-of-the-art performance on NTU and ModelNet40 datasets.

Effectively fuses multi-modal hypergraph information.

Reduces over-smoothing in deep hypergraph neural networks.

Abstract

Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains. Meanwhile, HyperGraph Neural Network (HGNN) is currently the de-facto method for hypergraph representation learning. However, HGNN aims at single hypergraph learning and uses a pre-concatenation approach when confronting multi-modal datasets, which leads to sub-optimal exploitation of the inter-correlations of multi-modal hypergraphs. HGNN also suffers the over-smoothing issue, that is, its performance drops significantly when layers are stacked up. To resolve these issues, we propose the Residual enhanced Multi-Hypergraph Neural Network, which can not only fuse multi-modal information from each hypergraph effectively, but also circumvent the over-smoothing issue associated with HGNN. We conduct experiments on two 3D benchmarks, the NTU and the ModelNet40 datasets, and compare against multiple state-of-the-art methods. Experimental results demonstrate that both the residual hypergraph convolutions and the multi-fusion architecture can improve the performance of the base model and the combined model achieves a new state-of-the-art. Code is available at \url{https://github.com/OneForward/ResMHGNN}.