- Paper link: https://www.ijcai.org/Proceedings/15/Papers/299.pdf
- Author's code repo: https://github.com/albertyang33/TADW. Note that the original code is implemented with MATLAB for the paper.
| Dataset | # Nodes | # Edges | # Classes |
|---|---|---|---|
| Cora | 2,708 | 10,556 | 7 |
| Citeseer | 3,327 | 9,228 | 6 |
Refer to Planetoid.
For all the datasets: The training ratio is 50% for linear SVM.
| Dataset | Paper(10%) | Paper(20%) | Paper(30%) | Paper(40%) | Paper(50%) | Our(tf) | Our(th) | Our(pd) | Our(ms) |
|---|---|---|---|---|---|---|---|---|---|
| Cora | 82.4 | 85.0 | 85.6 | 86.0 | 86.7 | 84.43(±0.37) | 84.42(±0.96) | 84.34(±0.64) | 84.08(±0.53) |
| Citeseer | 70.6 | 71.9 | 73.3 | 73.7 | 74.2 | 74.09(±1.11) | 74.41(±0.58) | 73.92(±0.72) | 73.87(±0.57) |
TL_BACKEND="tensorflow" python tadw_trainer.py --dataset cora --lr 0.2 --n_epoch 100 --embedding_dim 80 --lamda 0.2 --svdft 200
TL_BACKEND="torch" python tadw_trainer.py --dataset cora --lr 0.2 --n_epoch 100 --embedding_dim 80 --lamda 0.2 --svdft 200
TL_BACKEND="paddle" python tadw_trainer.py --dataset cora --lr 0.2 --n_epoch 100 --embedding_dim 80 --lamda 0.2 --svdft 200
TL_BACKEND="mindspore" python tadw_trainer.py --dataset cora --lr 0.2 --n_epoch 100 --embedding_dim 80 --lamda 0.2 --svdft 200
TL_BACKEND="torch" python tadw_trainer.py --dataset citeseer --lr 0.1 --n_epoch 50 --embedding_dim 500 --lamda 0.5 --svdft 300
TL_BACKEND="tensorflow" python tadw_trainer.py --dataset citeseer --lr 0.1 --n_epoch 50 --embedding_dim 500 --lamda 0.5 --svdft 300
TL_BACKEND="paddle" python tadw_trainer.py --dataset citeseer --lr 0.1 --n_epoch 50 --embedding_dim 500 --lamda 0.5 --svdft 300
TL_BACKEND="mindspore" python tadw_trainer.py --dataset citeseer --lr 0.1 --n_epoch 50 --embedding_dim 500 --lamda 0.5 --svdft 300