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Paper: Modeling Relational Data with Graph Convolutional Networks
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Author's code for entity classification: https://github.com/tkipf/relational-gcn
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Author's code for link prediction: https://github.com/MichSchli/RelationPrediction
| Dataset | #Nodes | #Edges | #Relations | #Labeled |
|---|---|---|---|---|
| AIFB | 8,285 | 58,086 | 90 | 176 |
| MUTAG | 23,644 | 148,454 | 46 | 340 |
| BGS | 333,845 | 1,832,398 | 206 | 146 |
| AM | 1,666,764 | 11,976,642 | 266 | 1000 |
| Dateset | #Nodes | #Node Types | #Edges | #Relations | Target | #Classes |
|---|---|---|---|---|---|---|
| IMDB | 11,616 | 3 | 102,804 | 4 | movie | 3 |
TL_BACKEND="torch" python rgcn_trainer.py --dataset aifb --l2 5e-5
TL_BACKEND="torch" python rgcn_trainer.py --dataset mutag --l2_coef 5e-2
TL_BACKEND="torch" python rgcn_trainer.py --dataset bgs --lr 0.0001 --l2_coef 5e-2
TL_BACKEND="torch" python rgcn_trainer.py --dataset imdb
TL_BACKEND="tensorflow" python rgcn_trainer.py --dataset aifb
TL_BACKEND="tensorflow" python rgcn_trainer.py --dataset mutag --l2_coef 5e-2
TL_BACKEND="tensorflow" python rgcn_trainer.py --dataset bgs --l2_coef 5e-2
TL_BACKEND="tensorflow" python rgcn_trainer.py --dataset imdb
TL_BACKEND="paddle" python rgcn_trainer.py --dataset aifb
TL_BACKEND="paddle" python rgcn_trainer.py --dataset mutag --l2_coef 5e-2
TL_BACKEND="paddle" python rgcn_trainer.py --dataset bgs --l2_coef 5e-2
TL_BACKEND="paddle" python rgcn_trainer.py --dataset imdb| Dataset | Paper | Our(th) | Our(tf) | Our(pd) |
|---|---|---|---|---|
| AIFB | 95.83 | 96.11(±1.52) | 94.17(±2.05) | 95.56(±2.3) |
| MUTAG | 73.23 | 85.0(±0.66) | 85.29(±1.20) | 85.00(±1.9) |
| BGS | 83.10 | 74.1(±1.7) | 73.79(±1.9) | 73.56(±3.8) |
| AM | 89.29 | |||
| IMDB | 48.54(±0.62) | 48.30(±1.20) | 48.44(±1.10) |