Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
This repository presents the experiments of the paper:
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
Russell Alan Hart, Linlin Yu, Yifei Lou, Feng Chen
Conference on Neural Information Processing Systems (NeurIPS), 2023.
[paper] [video] coming soon
To install requirements:
conda env create -f environment.yaml
conda activate gpn2
conda env list
To train the model(s) in the paper, run train_and_eval.py
Please cite our paper if you use the model or this code in your own work:
@inproceedings{
anonymous2023improvements,
title={Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization},
author={Anonymous},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=MUzdCW2hC6}
}
Our code is mostly adapted from :
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
Conference on Neural Information Processing Systems (NeurIPS) 2021.
Please also cite if you use the model or this code in your own work.
@incollection{graph-postnet,
title={Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification},
author={Stadler, Maximilian and Charpentier, Bertrand and Geisler, Simon and Z{\"u}gner, Daniel and G{\"u}nnemann, Stephan},
booktitle = {Advances in Neural Information Processing Systems},
volume = {34},
publisher = {Curran Associates, Inc.},
year = {2021}
}