- Paper link: https://arxiv.org/abs/2005.13183v3
- Author's code repo: https://github.com/kepsail/ie-HGCN. Note that the original code is implemented with Pytorch for the paper.
| Dataset | # Nodes | # Node Types | # Edges | # Edge Types | Target | # Classes |
|---|---|---|---|---|---|---|
| DBLP | 26,128 | 4 | 239,566 | 6 | author | 4 |
| IMDB | 21,420 | 4 | 86,642 | 6 | movie | 4 |
DBLP dataset refer to HGBDataset.
IMDBdataset refer to IMDB.
For the DBLP dataset: train test val = 974, 1420, 243 about 37% for training.
For the IMDB dataset: train test val = 400, 3478, 400, about 9% for training.
| Dataset | Paper(80% training) | Paper(60% training) | Paper(40% training) | Paper(20% training) | Our(tf) | Our(th) | Our(pd) |
|---|---|---|---|---|---|---|---|
| DBLP | 96.29 | 95.25 | 93.83 | 93.85 | 92.30±0.49% | 90.90±0.74% | 91.18±0.66% |
| IMDB | 58.35 | 60.84 | 59.81 | 56.60 | 58.10±0.42% | 55.22±1.21% | 56.08±2.13% |
TL_BACKEND="tensorflow" python3 iehgcn_trainer.py --dataset DBLP --n_epoch 30 --lr 0.01 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2
TL_BACKEND="torch" python3 iehgcn_trainer.py --dataset DBLP --n_epoch 30 --lr 0.005 --num_layers 4 --hidden_channels [64, 32, 16] --l2_coef 0.0005 --drop_rate 0.0
TL_BACKEND="paddle" python3 iehgcn_trainer.py --dataset DBLP --n_epoch 30 --lr 0.01 --num_layers 4 --hidden_channels [64, 32, 16] --l2_coef 0.0005 --drop_rate 0.1
TL_BACKEND="torch" python3 iehgcn_trainer.py --dataset IMDB --n_epoch 25 --lr 0.01 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2
TL_BACKEND="tensorflow" python3 iehgcn_trainer.py --dataset IMDB --n_epoch 25 --lr 0.005 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2
TL_BACKEND="paddle" python3 iehgcn_trainer.py --dataset IMDB --n_epoch 25 --lr 0.005 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2