Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

Last update: Dec 19, 2022

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Deep Learning CaGCN

Overview

CaGCN

This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".

Paper Link: https://arxiv.org/abs/2109.14285

Environment

python == 3.8.8
pytorch == 1.8.1
dgl -cuda11.1 == 0.6.1
networkx == 2.5
numpy == 1.20.2

GPU: GeForce RTX 2080 Ti

CPU: Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz

Confidence Calibration

CaGCN

python CaGCN.py --model GCN --hidden 64 --dataset dataset --labelrate labelrate --stage 1 --lr_for_cal 0.01 --l2_for_cal 5e-3
python CaGCN.py --model GAT --hidden 8 --dataset dataset --labelrate labelrate --dropout 0.6 --lr 0.005 --stage 1 --lr_for_cal 0.01 --l2_for_cal 5e-3

dataset: including [Cora, Citeseer, Pubmed], required.
labelrate: including [20, 40, 60], required.

e.g.,

python CaGCN.py --model GCN --hidden 64 --dataset Cora --labelrate 20 --stage 1 --lr_for_cal 0.01 --l2_for_cal 5e-3
python CaGCN.py --model GAT --hidden 8 --dataset Cora --labelrate 20 --dropout 0.6 --lr 0.005 --stage 1 --lr_for_cal 0.01 --l2_for_cal 5e-3

For CoraFull,

python CaGCN.py --model GCN --hidden 64 --dataset CoraFull --labelrate labelrate --stage 1 --lr_for_cal 0.01 --l2_for_cal 0.03
python CaGCN.py --model GAT --hidden 8 --dataset CoraFull --labelrate labelrate --dropout 0.6 --lr 0.005 --stage 1 --lr_for_cal 0.01 --l2_for_cal 0.03

labelrate: including [20, 40, 60], required.

Uncalibrated model

python train_others.py --model GCN --hidden 64 --dataset dataset --labelrate labelrate --stage 1 
python train_others.py --model GAT --hidden 8 --dataset dataset --labelrate labelrate --stage 1 --dropout 0.6 --lr 0.005

dataset: including [Cora, Citeseer, Pubmed, CoraFull], required.
labelrate: including [20, 40, 60], required.

e.g.,

python train_others.py --model GCN --hidden 64 --dataset Cora --labelrate 20 --stage 1
python train_others.py --model GAT --hidden 8 --dataset Cora --labelrate 20 --stage 1 --dropout 0.6 --lr 0.005

Temperature scaling & Matring Scaling

python train_others.py --model GCN --scaling_method method --hidden 64 --dataset dataset --labelrate labelrate --stage 1 --lr_for_cal 0.01 --max_iter 50
python train_others.py --model GAT --scaling_method method --hidden 8 --dataset dataset --labelrate labelrate --dropout 0.6 --lr 0.005 --stage 1 --lr_for_cal 0.01 --max_iter 50

method: including [TS, MS], required.
dataset: including [Cora, Citeseer, Pubmed, CoraFull], required.
labelrate: including [20, 40, 60], required.

e.g.,

python train_others.py --model GCN --scaling_method TS --hidden 64 --dataset Cora --labelrate 20 --stage 1 --lr_for_cal 0.01 --max_iter 50
python train_others.py --model GAT --scaling_method TS --hidden 8 --dataset Cora --labelrate 20 --dropout 0.6 --lr 0.005 --stage 1 --lr_for_cal 0.01 --max_iter 50

Self-Training

GCN L/C=20

python CaGCN.py --model GCN --hidden 64 --dataset Cora --labelrate 20 --stage 4 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 200 --threshold 0.8
python CaGCN.py --model GCN --hidden 64 --dataset Citeseer --labelrate 20 --stage 5 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 150 --threshold 0.9
python CaGCN.py --model GCN --hidden 64 --dataset Pubmed --labelrate 20 --stage 6 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 100 --threshold 0.8
python CaGCN.py --model GCN --hidden 64 --dataset CoraFull --labelrate 20 --stage 4 --lr_for_cal 0.001 --l2_for_cal 0.03 --epoch_for_st 500 --threshold 0.85

GCN L/C=40

python CaGCN.py --model GCN --hidden 64 --dataset Cora --labelrate 40 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 200 --threshold 0.8
python CaGCN.py --model GCN --hidden 64 --dataset Citeseer --labelrate 40 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 150 --threshold 0.85
python CaGCN.py --model GCN --hidden 64 --dataset Pubmed --labelrate 40 --stage 4 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 100 --threshold 0.8
python CaGCN.py --model GCN --hidden 64 --dataset CoraFull --labelrate 40 --stage 4 --lr_for_cal 0.001 --l2_for_cal 0.03 --epoch_for_st 500 --threshold 0.99

GCN L/C=60

python CaGCN.py --model GCN --hidden 64 --dataset Cora --labelrate 60 --stage 4 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 200 --threshold 0.8
python CaGCN.py --model GCN --hidden 64 --dataset Citeseer --labelrate 60 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 150 --threshold 0.8
python CaGCN.py --model GCN --hidden 64 --dataset Pubmed --labelrate 60 --stage 3 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 100 --threshold 0.6
python CaGCN.py --model GCN --hidden 64 --dataset CoraFull --labelrate 60 --stage 5 --lr_for_cal 0.001 --l2_for_cal 0.03 --epoch_for_st 500 --threshold 0.9

GAT L/C=20

python CaGCN.py --model GAT --hidden 8 --dataset Cora --labelrate 20 --dropout 0.6 --lr 0.005 --stage 6 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 200 --threshold 0.8
python CaGCN.py --model GAT --hidden 8 --dataset Citeseer --labelrate 20 --dropout 0.6 --lr 0.005 --stage 3 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 150 --threshold 0.7
python CaGCN.py --model GAT --hidden 8 --dataset Pubmed --labelrate 20 --dropout 0.6 --lr 0.005 --weight_decay 1e-3 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 100 --threshold 0.8 
python CaGCN.py --model GAT --hidden 8 --dataset CoraFull --labelrate 20 --dropout 0.6 --lr 0.005 --stage 5 --lr_for_cal 0.001 --l2_for_cal 0.03 --epoch_for_st 500 --threshold 0.95

GAT L/C=40

python CaGCN.py --model GAT --hidden 8 --dataset Cora --labelrate 40 --dropout 0.6 --lr 0.005 --stage 4 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 200 --threshold 0.9
python CaGCN.py --model GAT --hidden 8 --dataset Citeseer --labelrate 40 --dropout 0.6 --lr 0.005 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 150 --threshold 0.8
python CaGCN.py --model GAT --hidden 8 --dataset Pubmed --labelrate 40 --dropout 0.6 --lr 0.005 --weight_decay 1e-3 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 100 --threshold 0.8 
python CaGCN.py --model GAT --hidden 8 --dataset CoraFull --labelrate 40 --dropout 0.6 --lr 0.005 --stage 2 --lr_for_cal 0.001 --l2_for_cal 0.03 --epoch_for_st 500 --threshold 0.95

GAT L/C=60

python CaGCN.py --model GAT --hidden 8 --dataset Cora --labelrate 60 --dropout 0.6 --lr 0.005 --stage 2 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 200 --threshold 0.8
python CaGCN.py --model GAT --hidden 8 --dataset Citeseer --labelrate 60 --dropout 0.6 --lr 0.005 --stage 6 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 150 --threshold 0.8
python CaGCN.py --model GAT --hidden 8 --dataset Pubmed --labelrate 60 --dropout 0.6 --lr 0.005 --weight_decay 1e-3 --stage 3 --lr_for_cal 0.001 --l2_for_cal 5e-3 --epoch_for_st 100 --threshold 0.85 
python CaGCN.py --model GAT --hidden 8 --dataset CoraFull --labelrate 60 --dropout 0.6 --lr 0.005 --stage 2 --lr_for_cal 0.001 --l2_for_cal 0.03 --epoch_for_st 500 --threshold 0.95

More Parameters

For more parameters of baselines, please refer to the Parameter.md

Contact

If you have any questions, please feel free to contact me with [email protected]

Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

Related tags

Overview

CaGCN

Environment

Confidence Calibration

CaGCN

Uncalibrated model

Temperature scaling & Matring Scaling

Self-Training

GCN L/C=20

GCN L/C=40

GCN L/C=60

GAT L/C=20

GAT L/C=40

GAT L/C=60

More Parameters

Contact

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