- Paper link: https://arxiv.org/abs/1806.03536
- Author's code repo: https://github.com/ShinKyuY/Representation_Learning_on_Graphs_with_Jumping_Knowledge_Networks. Note that the original code is implemented with Tensorflow for the paper.
Run with following (available dataset: "cora", "citeseer", "pubmed")
python jknet_trainer.py --dataset cora For details settings, please refer to here.
TL_BACKEND="paddle" python jknet_trainer.py --dataset cora --mode max --lr 0.01 --n_epoch 170 --hidden_dim 32
TL_BACKEND="paddle" python jknet_trainer.py --dataset citeseer --mode max --lr 0.01 --n_epoch 200 --hidden_dim 64
TL_BACKEND="paddle" python jknet_trainer.py --dataset pubmed --mode cat --lr 0.01 --n_epoch 300 --hidden_dim 64 --itera_K 4
TL_BACKEND="tensorflow" python jknet_trainer.py --dataset cora --mode cat --lr 0.005 --n_epoch 200 --hidden_dim 64
TL_BACKEND="tensorflow" python jknet_trainer.py --dataset citeseer --mode cat --lr 0.01 --n_epoch 170 --hidden_dim 32
TL_BACKEND="tensorflow" python jknet_trainer.py --dataset pubmed --mode max --lr 0.01 --n_epoch 170 --hidden_dim 32
TL_BACKEND="torch" python jknet_trainer.py --dataset cora --mode max --lr 0.01 --n_epoch 200 --hidden_dim 16
TL_BACKEND="torch" python jknet_trainer.py --dataset citeseer --mode cat --lr 0.01 --n_epoch 200 --hidden_dim 16
TL_BACKEND="torch" python jknet_trainer.py --dataset pubmed --mode max --lr 0.1 --n_epoch 200 --hidden_dim 16| Dataset | Paper | Our(pd) | Our(tf) | Our(th) |
|---|---|---|---|---|
| cora | 0.896(±0.005) | 0.847(±0.01) | 0.8584(±0.007) | 0.872(±0.007) |
| citeseer | 0.783(±0.008) | 0.7554(±0.001) | 0.761(±0.01) | 0.769(±0.014) |
| pubmed | 0.7782(±0.003) | 0.7826(±0.005) | 0.792(±0.005) |