This repository provides a reference implementation of GNNs as described in the paper "p-Laplacian Based Graph Neural Networks" published at ICML'2022.
Install the following packages:
- pytorch 1.9.0
- torch_geometric 1.7.1
- scikit-learn 0.24.2
- networkx 2.5.1
$ python main.py --input cora --train_rate 0.025 --val_rate 0.025 --model pgnn --mu 0.1 --p 2 --K 4 --num_hid 16 --lr 0.01 --epochs 1000
$ bash run_test.sh
If you find GNNs useful in your research, please cite our paper:
@inproceedings{DBLP:conf/icml/FuZB22,
author = {Guoji Fu and
Peilin Zhao and
Yatao Bian},
editor = {Kamalika Chaudhuri and
Stefanie Jegelka and
Le Song and
Csaba Szepesv{\'{a}}ri and
Gang Niu and
Sivan Sabato},
title = {p-Laplacian Based Graph Neural Networks},
booktitle = {International Conference on Machine Learning, {ICML} 2022, 17-23 July
2022, Baltimore, Maryland, {USA}},
series = {Proceedings of Machine Learning Research},
volume = {162},
pages = {6878--6917},
publisher = {{PMLR}},
year = {2022},
url = {https://proceedings.mlr.press/v162/fu22e.html},
timestamp = {Tue, 12 Jul 2022 17:36:52 +0200},
biburl = {https://dblp.org/rec/conf/icml/FuZB22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}