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<h1 align="center">Graph State Space Convolution (GSSC)</h1> <p align="center"> <a href="https://arxiv.org/abs/2406.05815"><img src="https://img.shields.io/badge/-arXiv-grey?logo=gitbook&logoColor=white" alt="Paper"></a> <a href="https://github.com/Graph-COM/GSSC"><img src="https://img.shields.io/badge/-Github-grey?logo=github" alt="Github"></a> </p>

This repository contains the official implementation of GSSC as described in the paper: What Can We Learn from State Space Models for Machine Learning on Graphs? by Yinan Huang*, Siqi Miao*, and Pan Li.

(*Equal contribution, listed in alphabetical order)

Installation

All required packages are listed in environment.yml.

Running the code

Replace --cfg with the path to the configuration file and --device with the GPU device number like below:

python main.py --cfg configs/GSSC/peptides-func-GSSC.yaml --device 0 wandb.use False

This command will train the model on the peptides-func dataset using the GSSC method with default hyperparameters.

Reproducing the results

We use wandb to log and sweep the results. To reproduce the reported results, one needs to create and login to a wandb account. Then, one can launch the sweep using the configuration files in the configs directory. For example, to reproduce the tuned results of GSSC on the peptides-func dataset, one can launch the sweep using configs/GSSC/peptides-func-GSSC-tune.yaml.

Acknowledgement

This repository is built upon GraphGPS (Rampasek et al., 2022).