  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.
