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Code for the Paper "SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks"

This repository contains code for the paper "SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks", by Davide Buffelli, Pietro Lio, and Fabio Vandin, accepted at NeurIPS 2022. If you use this repository please cite this paper.

@inproceedings{
  buffelli2022sizeshiftreg,
  title={SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks},
  author={Davide Buffelli and Pietro Lio and Fabio Vandin},
  booktitle={Advances in Neural Information Processing Systems},
  editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
  year={2022},
  url={https://openreview.net/forum?id=wOI0AUAq9BR}
}

Part of this library is based on code from: https://github.com/PurdueMINDS/size-invariant-GNNs

Install

Please install the following dependencies to run this code:

• PyTorch 1.9.0
• torch-cluster 1.5.8
• torch-geometric 1.7.2
• torch-scatter 2.0.5
• torch-sparse 0.6.8
• torch-spline-conv 1.2.0
• ray[tune] 1.1.0

Install the additional dependencies as follows:

$ pip install -r requirements.txt

A snapshot of the environment used for our experiments can be found in the file env.txt.

Data

Download the data from: https://zenodo.org/record/7109766#.Yy7AGC8w1pQ Unzip the folder and put it inside the data folder in this repository. The path should be data/4/.. The folder data/ in this library contains the datasets. The path to this folder needs to be set in the configuration files in the configs folder.

Run Experiments

The configs folder contains configuration files for all the models and dataset. To run an experiment simply execute the command below, sobstituting {configuration_file_name} with the name of the configuration file for the experiment you are interested in. Important: before running the code below open the configuration file and modify the data_dir field so that it points to the path of the data folder contained in this repository.

$ python lightning_modules.py {configuration_file_name}

As an example we report the instructions to run experiments for PNA on all datasets, with (the ones with SSR suffix) and without our regularization:

$ python lightning_modules.py pna_dd
$ python lightning_modules.py pna_dd_ssr
$ python lightning_modules.py pna_proteins
$ python lightning_modules.py pna_proteins_ssr
$ python lightning_modules.py pna_nci1
$ python lightning_modules.py pna_nci1_ssr
$ python lightning_modules.py pna_nci109
$ python lightning_modules.py pna_nci109_ssr
$ python lightning_modules.py pna_deezer
$ python lightning_modules.py pna_deezer_ssr