dmlc--dgl
Quan (Andy) Gan
85 行
2.9 KiB
Markdown
85 行
2.9 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 96.29% (3 runs, 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 70.59% (3 runs, 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 93.10% (3 runs, 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
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```
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AM: accuracy 89.22% (3 runs, 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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### Entity Classification with minibatch
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AIFB: accuracy avg(5 runs) 90.00%, best 94.44% (DGL)
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```
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python3 entity_classify_mp.py -d aifb --testing --gpu 0 --fanout='20,20' --batch-size 128
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```
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MUTAG: accuracy avg(10 runs) 62.94%, best 72.06% (DGL)
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```
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python3 entity_classify_mp.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0 --batch-size 64 --fanout "-1, -1" --use-self-loop --dgl-sparse --n-epochs 20 --sparse-lr 0.01 --dropout 0.5
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```
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BGS: accuracy avg(5 runs) 78.62%, best 86.21% (DGL)
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```
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python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout "-1, -1" --n-epochs=16 --batch-size=16 --dgl-sparse --lr 0.01 --sparse-lr 0.05 --dropout 0.3
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```
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AM: accuracy avg(5 runs) 87.37%, best 89.9% (DGL)
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```
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python3 entity_classify_mp.py -d am --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout '35,35' --batch-size 64 --n-hidden 16 --use-self-loop --n-epochs=20 --dgl-sparse --lr 0.01 --sparse-lr 0.02 --dropout 0.7
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```
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### Entity Classification on OGBN-MAG
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Test-bd: P3-8xlarge
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OGBN-MAG accuracy 45.5 (3 runs)
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```
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python3 entity_classify_mp.py -d ogbn-mag --testing --fanout='30,30' --batch-size 1024 --n-hidden 128 --lr 0.01 --num-worker 4 --eval-batch-size 8 --low-mem --gpu 0,1,2,3 --dropout 0.7 --use-self-loop --n-bases 2 --n-epochs 3 --node-feats --dgl-sparse --sparse-lr 0.08
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```
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OGBN-MAG without node-feats 42.79
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```
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python3 entity_classify_mp.py -d ogbn-mag --testing --fanout='30,30' --batch-size 1024 --n-hidden 128 --lr 0.01 --num-worker 4 --eval-batch-size 8 --low-mem --gpu 0,1,2,3 --dropout 0.7 --use-self-loop --n-bases 2 --n-epochs 3 --dgl-sparse --sparse-lr 0.08
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```
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Test-bd: P2-8xlarge
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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 --eval-protocol 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 --eval-protocol filtered
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```
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