Hypergraphs, with hyperedges connecting more than two nodes, are key for modelling higher-order interactions in real-world data. The success of graph neural networks (GNNs) reveals the capability of neural networks to process data with pairwise interactions. This inspires the usage of neural networks for data with higher-order interactions, thereby leading to the development of hypergraph neural networks (HyperGNNs). GNNs and HyperGNNs are typically considered distinct since they are designed for data on different geometric topologies. However, in this paper, we theoretically demonstrate that, in the context of node classification, most HyperGNNs can be approximated using a GNN with a weighted clique expansion of the hypergraph. This leads to WCE-GNN, a simple and efficient framework comprising a GNN and a weighted clique expansion (WCE), for hypergraph node classification. Experiments on nine real-world hypergraph node classification benchmarks showcase that WCE-GNN demonstrates not only higher classification accuracy compared to state-of-the-art HyperGNNs, but also superior memory and runtime efficiency.

翻译：超图通过连接多个节点的超边，为建模真实世界数据中的高阶交互提供了关键途径。图神经网络（GNN）的成功揭示了神经网络处理成对交互数据的能力，这启发了将神经网络应用于高阶交互数据的研究，从而推动了超图神经网络（HyperGNN）的发展。GNN与HyperGNN通常被视为两类不同的模型，因为它们分别针对不同几何拓扑结构的数据设计。然而，本文在理论上证明：在节点分类任务中，大多数HyperGNN可通过在超图的加权团展开上构建GNN进行近似。基于此发现，我们提出WCE-GNN——一个由GNN与加权团展开（WCE）组成的简洁高效框架，用于超图节点分类。在九个真实超图节点分类基准上的实验表明，WCE-GNN不仅比现有最优HyperGNN具有更高的分类准确率，同时在内存与运行时间效率上展现出显著优势。