Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods.

翻译：近年来，图神经网络（GNN）因其在建模复杂图结构数据方面的能力与灵活性而日益受到关注。在所有图学习方法中，超图学习是一种在训练图嵌入空间时探索隐式高阶相关性的技术。本文提出了一种名为LFH的超图学习框架，该框架能够利用图的异质性属性进行动态超边构建和注意力嵌入更新。具体而言，在该框架中，首先通过成对融合策略利用显式图结构信息生成高质量特征，以得到初始节点嵌入。随后，通过隐式超边的动态分组构建超图，并执行类型特定的超图学习过程。为了评估所提框架的有效性，我们在多个流行数据集上，针对节点分类与链接预测两项任务，与包括同质成对图学习、异质成对图学习及超图学习在内的十一项最新模型进行了全面实验。实验结果表明，与近期最先进方法相比，该方法在节点分类和链接预测任务中分别实现了平均12.5%和13.3%的显著性能提升。