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Variational Graph Auto-Encoders (VGAE)

Dataset Statics

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.

Model

GAE

Results

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

Results

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