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

Related tags

Deep LearningCaGCN
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]

Owner
GitHub Repository
A TensorFlow implementation of the Mnemonic Descent Method.

MDM A Tensorflow implementation of the Mnemonic Descent Method. Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment G.

123 Oct 07, 2022
This is the official implementation for the paper "(Almost) Free Incentivized Exploration from Decentralized Learning Agents" in NeurIPS 2021.

Observe then Incentivize Experiments This is the code used for the paper "(Almost) Free Incentivized Exploration from Decentralized Learning Agents",

Cong Shen Research Group 0 Mar 08, 2022
Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression

Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression We provide the code used in our paper "How Good are Low-Rank Approximation

Aristeidis (Ares) Panos 0 Dec 13, 2021
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

U-GAT-IT — Official TensorFlow Implementation (ICLR 2020) : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization fo

Junho Kim 6.2k Jan 04, 2023
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN)

Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN) This is the implementation of the paper Multi-Age

Future Power Networks 83 Jan 06, 2023
Improving Non-autoregressive Generation with Mixup Training

MIST Training MIST TRAIN_FILE=/your/path/to/train.json VALID_FILE=/your/path/to/valid.json OUTPUT_DIR=/your/path/to/save_checkpoints CACHE_DIR=/your/p

7 Nov 22, 2022
《Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching》(CVPR 2020)

This contains the codes for cross-view geo-localization method described in: Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching, CVPR2020.

41 Oct 27, 2022
You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

Huiyiqianli 42 Dec 06, 2022
v objective diffusion inference code for PyTorch.

v-diffusion-pytorch v objective diffusion inference code for PyTorch, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The

Katherine Crowson 635 Dec 30, 2022
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

ActNN : Activation Compressed Training This is the official project repository for ActNN: Reducing Training Memory Footprint via 2-Bit Activation Comp

UC Berkeley RISE 178 Jan 05, 2023
alfred-py: A deep learning utility library for **human**

Alfred Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then a

JinTian 800 Jan 03, 2023
The Malware Open-source Threat Intelligence Family dataset contains 3,095 disarmed PE malware samples from 454 families

MOTIF Dataset The Malware Open-source Threat Intelligence Family (MOTIF) dataset contains 3,095 disarmed PE malware samples from 454 families, labeled

Booz Allen Hamilton 112 Dec 13, 2022
StyleTransfer - Open source style transfer project, based on VGG19

StyleTransfer - Open source style transfer project, based on VGG19

Patrick martins de lima 9 Dec 13, 2021
Convert onnx models to pytorch.

onnx2torch onnx2torch is an ONNX to PyTorch converter. Our converter: Is easy to use – Convert the ONNX model with the function call convert; Is easy

ENOT 264 Dec 30, 2022
Official Pytorch implementation for 2021 ICCV paper "Learning Motion Priors for 4D Human Body Capture in 3D Scenes" and trained models / data

Learning Motion Priors for 4D Human Body Capture in 3D Scenes (LEMO) Official Pytorch implementation for 2021 ICCV (oral) paper "Learning Motion Prior

165 Dec 19, 2022
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Preprocess file of the dataset used in implicit sub-populations: (Demographic groups

<a href=[email protected]"> 4 Oct 14, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022
All public open-source implementations of convnets benchmarks

convnet-benchmarks Easy benchmarking of all public open-source implementations of convnets. A summary is provided in the section below. Machine: 6-cor

Soumith Chintala 2.7k Dec 30, 2022
Check out the StyleGAN repo and place it in the same directory hierarchy as the present repo

Variational Model Inversion Attacks Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani Most commands are in run_scripts. W

Jackson Wang 15 Dec 26, 2022