- Paper: Modeling Relational Data with Graph Convolutional Networks
- Author's code for entity classification: https://github.com/tkipf/relational-gcn
- Author's code for link prediction: https://github.com/MichSchli/RelationPrediction
- PyTorch 1.10
- rdflib
- pandas
- tqdm
- TorchMetrics
pip install rdflib pandas
Example code was tested with rdflib 4.2.2 and pandas 0.23.4
For AIFB, MUTAG, BGS and AM,
python entity.py -d aifb --wd 0 --gpu 0
python entity.py -d mutag --n-bases 30 --gpu 0
python entity.py -d bgs --n-bases 40 --gpu 0
python entity.py -d am --n-bases 40 --n-hidden 10 --gpu 0
For AIFB, MUTAG, BGS and AM,
python entity_sample.py -d aifb --wd 0 --gpu 0 --fanout='20,20' --batch-size 128
python entity_sample.py -d mutag --n-bases 30 --gpu 0 --batch-size 64 --fanout='-1,-1' --use-self-loop --n-epochs 20 --dropout 0.5
python entity_sample.py -d bgs --n-bases 40 --gpu 0 --fanout='-1,-1' --n-epochs=16 --batch-size=16 --dropout 0.3
python entity_sample.py -d am --n-bases 40 --gpu 0 --fanout='35,35' --batch-size 64 --n-hidden 16 --use-self-loop --n-epochs=20 --dropout 0.7
To use multiple GPUs, replace entity_sample.py with entity_sample_multi_gpu.py and specify
multiple GPU IDs separated by comma, e.g., --gpu 0,1.
FB15k-237 in RAW-MRR
python link.py --gpu 0 --eval-protocol raw
FB15k-237 in Filtered-MRR
python link.py --gpu 0 --eval-protocol filtered