articleApr 20, 2020GOLD OA

Heterogeneous Graph Transformer

ZHZiniu HuYDYuxiao DongKWKuansan WangYSYizhou Sun

University of California, Los Angeles · Microsoft Research (United Kingdom)

Indexed incrossref

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 it 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 Web-scale graph data, we design the heterogeneous mini-batch graph…

Citation impact

1,295
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FWCI
81.96
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100%
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24
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Authors

4

Topics & keywords

Topics
Keywords
  • Computer science
  • Scalability
  • Theoretical computer science
  • Graph
  • Homogeneous
  • Transformer
  • Distributed computing
  • Data mining
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