  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.
