Graph embedding analyzes network data by learning the vector representation of each vertex in the network, and has attracted widespread attention in recent years. In many real-world networks, considering the topology and attributes of nodes comprehensively has potential value in achieving more effective graph embedding. However, the existing methods do not fully utilize and integrate these two kinds of information. The main challenges include three aspects: sparseness and noise in structure information; insufficient modeling of non-linear relationship between nodes in attribute semantic space; heterogeneity of structure and attribute information. To address these issues, based on graph neural network and hypergraph, we propose a Dual-view HyperGraph Neural Network (DHGNN) model for attributed graph learning. First, we unify the expression form of different information sources of nodes by hypergraph, and construct dual hypergraphs according to topology and attributes of nodes. Secondly, we propose a dual-view hypergraph neural network for graph embedding. The central idea is that we model and integrate different information sources by shared and specific hypergraph convolutional layer, and use the attention mechanism to adequately combine dual node embeddings. Finally, we train the model through semi-supervised node classification task. Extensive experiments have been carried out on four real world public datasets, demonstrating the performance of the proposed model DHGNN has always been superior to that of state-of-the-art graph embedding methods.

图嵌入通过学习网络中每个顶点的向量表示来分析网络数据，近年来受到广泛关注。在许多现实世界的网络中，综合考虑节点的拓扑和属性对于实现更有效的图嵌入具有潜在价值。然而，现有的方法并没有充分利用和整合这两种信息。主要挑战包括三个方面：结构信息的稀疏性和噪声；属性语义空间中节点之间的非线性关系建模不足；结构和属性信息的异质性。为了解决这些问题，基于图形神经网络和超图上，我们提出了一个d UAL视^ h yper摹拉夫ñ eural ñ用于属性图学习的网络（DHGNN）模型。首先，我们用超图统一了节点不同信息源的表达形式，并根据节点的拓扑结构和属性构造了双超图。其次，我们提出了一种用于图嵌入的双视图超图神经网络。中心思想是我们通过共享和特定的超图卷积层对不同的信息源进行建模和整合，并使用注意力机制充分结合双节点嵌入。最后，我们通过半监督节点分类任务训练模型。已经在四个真实世界的公共数据集上进行了大量实验，表明所提出的模型 DHGNN 的性能始终优于最先进的图嵌入方法。