The document proposes a Heterogeneous Graph Transformer (HGT) model for tasks on heterogeneous graphs like the Open Academic Graph. HGT uses heterogeneous mutual attention to aggregate information from different node types, heterogeneous message passing to propagate information, and target-specific aggregation to generate embeddings. It is evaluated on several node classification, link prediction, and author disambiguation tasks, outperforming baselines like GCN, GAT, R-GCN, HetGNN and HAN.

1. Van Thuy Hoang
Dept. of Artificial Intelligence,
The Catholic University of Korea
hoangvanthuy90@gmail.com

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Background
 General GNN Framework

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Background
 Graph Attention Network

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Background
 Relational graph convolutional networks (R-GCN)

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Heterogeneous Information
Networks (HIN)
 In real word, graphs are heterogeneous

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Open Academic Graph
 HTG takes only its one hop edges as inpout without metapath

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OAG Graph
 Many entities

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Tasks
 Node Classification
 Paper-Field prediction
 Paper–Field (L1)
 Paper–Field (L2)
 Paper-Venue prediction
 Link prediction
 Author Disambiguation tasks

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Heterogeneous Graph
 v =< Heterogeneous Graph Transformer >
 e = <HGT, HAN>

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Meta Relation

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Model
 Heterogeneous Mutual Attention
 Heterogeneous Message Passing
 Target-Specific Aggregation

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Heterogeneous Mutual Attention

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Heterogeneous Mutual Attention
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Heterogeneous Message Passing

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Target-Specific Aggregation

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Overall Architecture
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HGSampling
 keep a similar number of nodes and edges for each type
 keep the sampled sub-graph dense to minimize the information loss and
reduce the sample variance.

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Baselines
 GCN
 GAT
 R-GCN
 HetGNN (KDD19 Heterogeneous Graph Neural Network)
 HAN (WWW19 Heterogeneous Graph Attention Network)

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Results
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