{
  "id": "2105.00490",
  "version": "v1",
  "published": "2021-05-02T14:53:32.000Z",
  "updated": "2021-05-02T14:53:32.000Z",
  "title": "Residual Enhanced Multi-Hypergraph Neural Network",
  "authors": [
    "Jing Huang",
    "Xiaolin Huang",
    "Jie Yang"
  ],
  "comment": "ICIP 2021 submitted",
  "categories": [
    "cs.CV"
  ],
  "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}.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-05-02T14:53:32.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "residual enhanced multi-hypergraph neural network",
      "experimental results demonstrate",
      "over-smoothing issue",
      "model higher-order correlations",
      "multiple state-of-the-art methods"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}