Hypergraphs provide an effective modeling approach for modeling high-order relationships in many real-world datasets. To capture such complex relationships, several hypergraph neural networks have been proposed for learning hypergraph structure, which propagate information from nodes to hyperedges and then from hyperedges back to nodes. However, most existing methods focus on information propagation between hyperedges and nodes, neglecting the interactions among hyperedges themselves. In this paper, we propose HeIHNN, a hyperedge interaction-aware hypergraph neural network, which captures the interactions among hyperedges during the convolution process and introduce a novel mechanism to enhance information flow between hyperedges and nodes. Specifically, HeIHNN integrates the interactions between hyperedges into the hypergraph convolution by constructing a three-stage information propagation process. After propagating information from nodes to hyperedges, we introduce a hyperedge-level convolution to update the hyperedge embeddings. Finally, the embeddings that capture rich information from the interaction among hyperedges will be utilized to update the node embeddings. Additionally, we introduce a hyperedge outlier removal mechanism in the information propagation stages between nodes and hyperedges, which dynamically adjusts the hypergraph structure using the learned embeddings, effectively removing outliers. Extensive experiments conducted on real-world datasets show the competitive performance of HeIHNN compared with state-of-the-art methods.

翻译：超图为建模许多真实世界数据集中的高阶关系提供了有效的建模方法。为捕捉这种复杂关系，研究者提出了多种超图神经网络来学习超图结构，其通过节点到超边、再由超边到节点的信息传播方式实现。然而，现有方法大多聚焦于超边与节点间的信息传播，忽略了超边之间的相互作用。本文提出HeIHNN——一种超边交互感知的超图神经网络，该网络在卷积过程中捕捉超边间的相互作用，并引入新颖机制以增强超边与节点间的信息流动。具体而言，HeIHNN通过构建三阶段信息传播过程，将超边间的交互融入超图卷积：在完成节点到超边的信息传播后，引入超边级卷积更新超边嵌入；最终，利用融合了超边交互丰富信息的嵌入更新节点嵌入。此外，在节点与超边的信息传播阶段，我们引入超边异常值移除机制，该机制通过学习到的嵌入动态调整超图结构，有效去除异常值。在真实数据集上的大量实验表明，HeIHNN相比现有最先进方法展现出具有竞争力的性能。