Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
Guangyu Zhou eaa51ac4dc | 3 months ago | |
---|---|---|
.. | ||
readme.md | 1 year ago | |
vgae_trainer.py | 3 months ago |
Dataset | # Nodes | # Edges | # Classes |
---|---|---|---|
Cora | 2,708 | 10,556 | 7 |
Citeseer | 3,327 | 9,228 | 6 |
Pubmed | 19,717 | 88,651 | 3 |
Refer to Planetoid.
GAE* denotes experiments without using input features, GAE and VGAE use input features.
We report area under the ROC curve (AUC) and average precision (AP) scores for each model on the test set.
# available dataset: "cora", "citeseer", "pubmed"
# GAE model with input features
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset cora --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset citeseer --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset pubmed --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset cora --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset citeseer --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset pubmed --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset cora --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset citeseer --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset pubmed --model GAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
Dataset | Paper(GAE)(AUC,AP) | Our(tf)(GAE)(AUC,AP) | Our(th)(GAE)(AUC,AP) | Our(pd)(GAE)(AUC,AP) |
---|---|---|---|---|
cora | 91.0 92.0 | 91.30±0.85 92.42±0.43 | 92.02±0.44 93.12±0.16 | 91.16±0.73 92.04±0.87 |
citeseer | 89.5 89.9 | 87.06±0.14 88.18±0.26 | 89.62±0.48 89.86±0.73 | 89.61±1.34 90.09±1.56 |
pubmed | 96.4 96.5 | 97.06±0.32 96.68±0.31 | 97.11±0.56 97.13±0.23 | 96.25±0.29 96.35±0.34 |
# available dataset: "cora", "citeseer", "pubmed"
# GAE model without input features
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset cora --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset citeseer --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset pubmed --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset cora --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset citeseer --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset pubmed --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset cora --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset citeseer --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset pubmed --model GAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
Dataset | Paper(GAE*)(AUC,AP) | Our(tf)(GAE)(AUC,AP) | Our(th)(GAE)(AUC,AP) | Our(pd)(GAE)(AUC,AP) |
---|---|---|---|---|
cora | 84.3 88.1 | 85.88±0.22 89.55±0.77 | 83.78±0.71 87.28±0.88 | 85.56±1.41 89.28±1.15 |
citeseer | 78.7 84.1 | 77.45±0.66 83.76±0.32 | 78.23±0.19 85.21±0.47 | 78.91±1.40 83.93±0.65 |
pubmed | 82.2 87.4 | 83.02±0.13 87.32±0.55 | 83.53±0.29 87.95±0.66 | 80.62±0.68 86.58±0.47 |
VGAE* denotes experiments without using input features, GAE and VGAE use input features.
We report area under the ROC curve (AUC) and average precision (AP) scores for each model on the test set.
# available dataset: "cora", "citeseer", "pubmed"
# VGAE model with input features
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset cora --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset citeseer --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset pubmed --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset cora --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset citeseer --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset pubmed --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset cora --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset citeseer --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset pubmed --model VGAE --features 1 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
Dataset | Paper(VGAE)(AUC,AP) | Our(tf)(VGAE)(AUC,AP) | Our(th)(VGAE)(AUC,AP) | Our(pd)(VGAE)(AUC,AP) |
---|---|---|---|---|
cora | 91.4 92.6 | 92.91±0.62 93.99±0.87 | 90.80±0.32 91.51±0.74 | 91.42±0.23 92.56±0.54 |
citeseer | 90.8 92.0 | 91.48±0.56 93.11±0.12 | 90.81±0.34 91.99±0.47 | 90.39±1.27 91.32±1.49 |
pubmed | 94.4 94.7 | 93.91±0.72 93.79±0.65 | 94.45±0.24 94.86±0.35 | 95.41±0.16 95.48±0.20 |
# available dataset: "cora", "citeseer", "pubmed"
# VGAE model without input features
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset cora --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset citeseer --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="tensorflow" python vgae_trainer.py --dataset pubmed --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset cora --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset citeseer --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="torch" python vgae_trainer.py --dataset pubmed --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset cora --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset citeseer --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
TL_BACKEND="paddle" python vgae_trainer.py --dataset pubmed --model VGAE --features 0 --num_layers 2 --lr 0.01 --l2_coef 0. --drop_rate 0.
Dataset | Paper(VGAE*)(AUC,AP) | Our(tf)(VGAE)(AUC,AP) | Our(th)(VGAE)(AUC,AP) | Our(pd)(VGAE)(AUC,AP) |
---|---|---|---|---|
cora | 84.0 87.7 | 84.35±0.21 88.11±0.68 | 83.42±0.82 88.05±0.27 | 84.76±0.76 88.04±0.70 |
citeseer | 78.9 84.1 | 79.27±0.36 83.36±0.52 | 79.91±0.26 84.33±0.27 | 77.13±0.91 81.84±0.63 |
pubmed | 82.7 87.5 | 82.97±0.51 86.95±0.86 | 81.97±0.78 86.96±0.15 | 84.53±3.74 86.60±0.55 |
No Description
Python C++ Cuda Markdown Text
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》