dmlc--dgl
Ziniu Hu
ac282a5e35
* add HGT example * Update README.md * Update model.py * Update train_acm.py * Add comments Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
19 行
911 B
Markdown
19 行
911 B
Markdown
# Heterogeneous Graph Transformer (HGT)
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[Alternative PyTorch-Geometric implementation](https://github.com/acbull/pyHGT)
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[“**Heterogeneous Graph Transformer**”](https://arxiv.org/abs/2003.01332) is a graph neural network architecture that can deal with large-scale heterogeneous and dynamic graphs.
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This toy experiment is based on DGL's official [tutorial](https://docs.dgl.ai/en/0.4.x/generated/dgl.heterograph.html). As the ACM datasets doesn't have input feature, we simply randomly assign features for each node. Such process can be simply replaced by any prepared features.
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The reference performance against R-GCN and MLP running 5 times:
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| Model | Test Accuracy | # Parameter |
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| --------- | --------------- | -------------|
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| 2-layer HGT | 0.465 ± 0.007 | 2,176,324 |
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| 2-layer RGCN | 0.392 ± 0.013 | 416,340 |
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| MLP | 0.132 ± 0.003 | 200,974 |
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