dmlc--dgl
hgalioulline
6c7c403914
* Add filtered metrics for R-GCN example * Add new line to end of file * Add evaluation protocol argument option for R-GCN example * Update README Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com> Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>
49 行
1.3 KiB
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49 行
1.3 KiB
Markdown
# Relational-GCN
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* Paper: [https://arxiv.org/abs/1703.06103](https://arxiv.org/abs/1703.06103)
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* Author's code for entity classification: [https://github.com/tkipf/relational-gcn](https://github.com/tkipf/relational-gcn)
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* Author's code for link prediction: [https://github.com/MichSchli/RelationPrediction](https://github.com/MichSchli/RelationPrediction)
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### Dependencies
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* PyTorch 0.4.1+
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* requests
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* rdflib
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* pandas
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```
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pip install requests torch rdflib pandas
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```
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Example code was tested with rdflib 4.2.2 and pandas 0.23.4
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### Entity Classification
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AIFB: accuracy 97.22% (DGL), 95.83% (paper)
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```
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python3 entity_classify.py -d aifb --testing --gpu 0
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```
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MUTAG: accuracy 75% (DGL), 73.23% (paper)
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```
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python3 entity_classify.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0
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```
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BGS: accuracy 82.76% (DGL), 83.10% (paper)
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```
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python3 entity_classify.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --relabel
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```
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AM: accuracy 87.37% (DGL), 89.29% (paper)
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```
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python3 entity_classify.py -d am --n-bases=40 --n-hidden=10 --l2norm=5e-4 --testing
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```
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### Link Prediction
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FB15k-237: MRR 0.151 (DGL), 0.158 (paper)
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```
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python3 link_predict.py -d FB15k-237 --gpu 0 --raw
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```
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FB15k-237: Filtered-MRR 0.2044
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```
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python3 link_predict.py -d FB15k-237 --gpu 0 --filtered
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```
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