Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original intrinsic or artificially established graph structure which may not accurately reflect the "true" correlation among data and are not optimal for semi-supervised node classification in the downstream graph neural networks. Besides, while existing graph-based methods mostly utilize the explicit graph structure, some implicit information, for example, the density information, can also provide latent information that can be further exploited. To address these limitations, this paper proposes the Dual Hypergraph Neural Network (DualHGNN), a new dual connection model integrating both hypergraph structure learning and hypergraph representation learning simultaneously in a unified architecture. The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization. Then, DualHGNN employs a density-aware hypergraph attention network to explore the high-order semantic correlation among data points based on the density-aware attention mechanism. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.

翻译：基于图的半监督节点分类已被证明是许多应用中具有高研究价值和重要意义的前沿方法。现有方法大多仅基于原始固有或人工构建的图结构，这些结构可能无法准确反映数据间的"真实"关联性，且对于下游图神经网络中的半监督节点分类并非最优。此外，现有基于图的方法主要利用显式图结构，而隐含信息（例如密度信息）也可提供可供进一步挖掘的潜在信息。为解决这些局限性，本文提出双超图神经网络（DualHGNN），一种在统一架构中同时整合超图结构学习与超图表示学习的双连接模型。DualHGNN首先利用多视图超图学习网络从多个视角探索最优超图结构，并通过一致性损失约束以提升其泛化能力；随后，DualHGNN采用密度感知超图注意力网络，基于密度感知注意力机制探索数据点间的高阶语义关联。在多个基准数据集上进行的广泛实验结果表明了所提方法的有效性。