Residual Enhanced Multi-Hypergraph Neural Network

• URL: http://arxiv.org/abs/2105.00490v1
• Date: Sun, 2 May 2021 14:53:32 GMT
• Title: Residual Enhanced Multi-Hypergraph Neural Network
• Authors: Jing Huang, Xiaolin Huang and Jie Yang
• Abstract summary: HyperGraph Neural Network (HGNN) is the de-facto method for hypergraph representation learning. We propose the Residual enhanced Multi-Hypergraph Neural Network, which can fuse multi-modal information from each hypergraph effectively.
• Score: 26.42547421121713
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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}.

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