wehub-resource-sync
79 lines
1.9 KiB
Markdown
79 lines
1.9 KiB
Markdown
# DGL Implementation of the CompGCN Paper
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This DGL example implements the GNN model proposed in the paper [CompositionGCN](https://arxiv.org/abs/1911.03082).
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The author's codes of implementation is in [here](https://github.com/malllabiisc/CompGCN)
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Example implementor
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----------------------
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This example was implemented by [zhjwy9343](https://github.com/zhjwy9343) and [KounianhuaDu](https://github.com/KounianhuaDu) at the AWS Shanghai AI Lab.
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Dependencies
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----------------------
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- pytorch 1.9.0
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- dgl 0.7.1
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- numpy 1.20.3
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- ordered_set 4.0.2
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Dataset
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---------------------------------------
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The datasets used for link predictions are FB15k-237 constructed from Freebase and WN18RR constructed from WordNet. The statistics are summarized as followings:
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**FB15k-237**
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- Nodes: 14541
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- Relation types: 237
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- Reversed relation types: 237
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- Train: 272115
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- Valid: 17535
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- Test: 20466
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**WN18RR**
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- Nodes: 40943
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- Relation types: 11
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- Reversed relation types: 11
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- Train: 86835
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- Valid: 3034
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- Test: 3134
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How to run
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First to get the data, one can run
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```python
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sh get_fb15k-237.sh
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```
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```python
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sh get_wn18rr.sh
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```
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Then for FB15k-237, run
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```python
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python main.py --score_func conve --opn ccorr --gpu 0 --data FB15k-237
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```
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For WN18RR, run
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```python
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python main.py --score_func conve --opn ccorr --gpu 0 --data wn18rr
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```
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Performance
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-------------------------
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**Link Prediction Results**
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| Dataset | FB15k-237 | WN18RR |
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|---------| ------------------------ | ------------------------ |
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| Metric | Paper / ours (dgl) | Paper / ours (dgl) |
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| MRR | 0.355 / 0.348 | 0.479 / 0.466 |
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| MR | 197 / 208 | 3533 / 3542 |
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| Hit@10 | 0.535 / 0.527 | 0.546 / 0.525 |
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| Hit@3 | 0.390 / 0.380 | 0.494 / 0.476 |
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| Hit@1 | 0.264 / 0.259 | 0.443 / 0.435 |
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