Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for various downstream tasks like node classification. However, the message passing module in existing HNNs typically requires a computationally intensive training process, which limits their practical use. To tackle this challenge, we propose an alternative approach by decoupling the usage of hypergraph structural information from the model learning stage. This leads to a novel training-free message passing module, named TF-MP-Module, which can be precomputed in the data preprocessing stage, thereby reducing the computational burden. We refer to the hypergraph neural network equipped with our TF-MP-Module as TF-HNN. We theoretically support the efficiency and effectiveness of TF-HNN by showing that: 1) It is more training-efficient compared to existing HNNs; 2) It utilises as much information as existing HNNs for node feature generation; and 3) It is robust against the oversmoothing issue while using long-range interactions. Experiments based on seven real-world hypergraph benchmarks in node classification and hyperlink prediction show that, compared to state-of-the-art HNNs, TF-HNN exhibits both competitive performance and superior training efficiency. Specifically, on the large-scale benchmark, Trivago, TF-HNN outperforms the node classification accuracy of the best baseline by 10% with just 1% of the training time of that baseline.

翻译：超图对于建模现实世界数据中的高阶交互至关重要。超图神经网络通过消息传递有效利用这些结构，为节点分类等各种下游任务生成信息丰富的节点特征。然而，现有超图神经网络中的消息传递模块通常需要计算密集型的训练过程，这限制了其实际应用。为应对这一挑战，我们提出一种替代方法，将超图结构信息的使用与模型学习阶段解耦。这催生了一种新颖的无需训练的消息传递模块，称为TF-MP-Module，该模块可在数据预处理阶段预先计算，从而减轻计算负担。我们将配备TF-MP-Module的超图神经网络称为TF-HNN。我们从理论上支持TF-HNN的效率和有效性，证明：1）与现有超图神经网络相比，其训练效率更高；2）在节点特征生成方面，其利用的信息量与现有超图神经网络相当；3）在使用长程交互时，其对过度平滑问题具有鲁棒性。基于七个真实世界超图基准在节点分类和超链接预测上的实验表明，与最先进的超图神经网络相比，TF-HNN在保持竞争性性能的同时，展现出更优越的训练效率。具体而言，在大规模基准数据集Trivago上，TF-HNN仅用最佳基线1%的训练时间，就在节点分类准确率上超出该基线10%。