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
Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Muhammad Maaz 206 Jan 04, 2023
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le

deargen 11 Nov 19, 2022
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022
Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study

Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study Supplementary Materials for Kentaro Matsuura, Junya Honda, Imad

Kentaro Matsuura 4 Nov 01, 2022
4th place solution to datafactory challenge by Intermarché.

Solution to Datafactory challenge by Intermarché. 4th place solution to datafactory challenge by Intermarché. The objective of the challenge is to pre

Raphael Sourty 11 Mar 19, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Introduction This repository includes the source code for "Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks", which is pu

machen 11 Nov 27, 2022
Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Beyond the Spectrum Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis" by Yang He, Ning Yu, Margret Keu

Yang He 27 Jan 07, 2023
Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

PatchGraph This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Installation Creat

Paloma Sodhi 11 Dec 15, 2022
GRF: Learning a General Radiance Field for 3D Representation and Rendering

GRF: Learning a General Radiance Field for 3D Representation and Rendering [Paper] [Video] GRF: Learning a General Radiance Field for 3D Representatio

Alex Trevithick 243 Dec 29, 2022
Open source code for the paper of Neural Sparse Voxel Fields.

Neural Sparse Voxel Fields (NSVF) Project Page | Video | Paper | Data Photo-realistic free-viewpoint rendering of real-world scenes using classical co

Meta Research 647 Dec 27, 2022
The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Balloon Learning Environment Docs The Balloon Learning Environment (BLE) is a simulator for stratospheric balloons. It is designed as a benchmark envi

Google 87 Dec 25, 2022
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations

VirTex: Learning Visual Representations from Textual Annotations Karan Desai and Justin Johnson University of Michigan CVPR 2021 arxiv.org/abs/2006.06

Karan Desai 533 Dec 24, 2022
Code for "Universal inference meets random projections: a scalable test for log-concavity"

How to use this repository This repository contains code to replicate the results of "Universal inference meets random projections: a scalable test fo

Robin Dunn 0 Nov 21, 2021
Back to Basics: Efficient Network Compression via IMP

Back to Basics: Efficient Network Compression via IMP Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta This repository contains the code to r

IOL Lab @ ZIB 1 Nov 19, 2021
LSTM model trained on a small dataset of 3000 names written in PyTorch

LSTM model trained on a small dataset of 3000 names. Model generates names from model by selecting one out of top 3 letters suggested by model at a time until an EOS (End Of Sentence) character is no

Sahil Lamba 1 Dec 20, 2021
This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

DendroMap DendroMap is an interactive tool to explore large-scale image datasets used for machine learning. A deep understanding of your data can be v

DIV Lab 33 Dec 30, 2022
Exponential Graph is Provably Efficient for Decentralized Deep Training

Exponential Graph is Provably Efficient for Decentralized Deep Training This code repository is for the paper Exponential Graph is Provably Efficient

3 Apr 20, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
Python implementation of the multistate Bennett acceptance ratio (MBAR)

pymbar Python implementation of the multistate Bennett acceptance ratio (MBAR) method for estimating expectations and free energy differences from equ

Chodera lab // Memorial Sloan Kettering Cancer Center 169 Dec 02, 2022
Invert and perturb GAN images for test-time ensembling

GAN Ensembling Project Page | Paper | Bibtex Ensembling with Deep Generative Views. Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhan

Lucy Chai 93 Dec 08, 2022