标题： 异构图变换器 显示英文标题

标题： Heterogeneous Graph Transformer

摘要： 近年来，图神经网络（GNNs）在建模结构化数据方面取得了新兴的成功。然而，大多数GNNs是为同构图设计的，在同构图中所有节点和边属于同一类型，这使得它们无法表示异构结构。在本文中，我们提出了异构图变压器（HGT）架构来建模大规模异构图。为了建模异构性，我们设计了依赖于节点类型和边类型的参数，以表征每条边上的异构注意力，使HGT能够为不同类型的节点和边保持专用表示。为了处理动态异构图，我们将相对时间编码技术引入HGT，该技术能够捕捉任意持续时间的动态结构依赖性。为了处理大规模图数据，我们设计了异构小批量图采样算法---HGSampling---以实现高效和可扩展的训练。在包含1.79亿个节点和20亿条边的开放学术图上的大量实验表明，所提出的HGT模型在各种下游任务中始终比所有最先进的GNN基线模型高出9%--21%。 显示英文摘要

摘要： Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making them infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To model heterogeneity, we design node- and edge-type dependent parameters to characterize the heterogeneous attention over each edge, empowering HGT to maintain dedicated representations for different types of nodes and edges. To handle dynamic heterogeneous graphs, we introduce the relative temporal encoding technique into HGT, which is able to capture the dynamic structural dependency with arbitrary durations. To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm---HGSampling---for efficient and scalable training. Extensive experiments on the Open Academic Graph of 179 million nodes and 2 billion edges show that the proposed HGT model consistently outperforms all the state-of-the-art GNN baselines by 9%--21% on various downstream tasks.