HGNN-AS: Enhancing Hypergraph Neural Network for Node Classification Accuracy with Attention and Self-Attention

The incorporation of attention mechanisms into hypergraphs is an effective method for obtaining appropriate node and hyperedge representations. However, the ability of attention mechanisms to aggregate features on a hypergraph can be improved, particularly for noisy hypergraphs with connections between unrelated nodes that were constructed in a KNN-like manner. In this paper, we propose HGNN-AS, an enhanced hypergraph neural network model that achieves good accuracy on node classification tasks by combining an attention mechanism and a self-attention mechanism. Specifically, we introduce two self-attention mechanisms to improve how the HGNN-AS model expresses attention when distinguishing mislinked neighbours. Moreover, we add multihead attention mechanisms to our model to stabilize the training effect. The proposed model is evaluated on benchmark node classification tasks, including citation network classification and visual object recognition. Our experimental results demonstrate that our model outperforms most advanced methods on both tasks; among them, the accuracy improvement on the Cora dataset is the most noticeable, with an accuracy of 83.9%.