Heterogeneous Graph Transformer

Abstract

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.

Published on WWW 2020

References in corpus (5)

• Decoupled Weight Decay Regularization
• Inductive Representation Learning on Large Graphs
• Fast Graph Representation Learning with PyTorch Geometric
• Graph Transformer Networks
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Cited by in corpus (10)

• DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
• Graph Neural Networks: Taxonomy, Advances and Trends
• A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
• GPT-GNN: Generative Pre-Training of Graph Neural Networks
• Graph-Guided Network for Irregularly Sampled Multivariate Time Series
• Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction
• Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
• Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
• Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
• Network representation learning: A macro and micro view