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https://github.com/edisonleeeee/graphssm

[NeurIPS 2024] State Space Models on Temporal Graphs: A First-Principles Study
https://github.com/edisonleeeee/graphssm

state-space-model temporal-graph-learning temporal-graphs

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[NeurIPS 2024] State Space Models on Temporal Graphs: A First-Principles Study

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🐍GraphSSM (Graph State Space Models)


State Space Models on Temporal Graphs: A First-Principles Study

Jintang Li1, Ruofan Wu2, Boqun Ma3, Xinzhou Jin1, Liang Chen1, Zibin Zheng1

1Sun Yat-sen University, 2Coupang, 3Shanghai Jiao Tong University

[![arXiv](https://img.shields.io/badge/arXiv-2406.00943-b31b1b.svg)](https://arxiv.org/abs/2406.00943)

## Environments
> [!NOTE]
> Higher versions should be also compatible.

```
conda create -n GraphSSM python=3.10
conda activate GraphSSM

pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu121
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.2.0+cu121.html

pip install -r requirements.txt
```

## Reproduction

```
bash scripts/s4.sh
bash scripts/s5.sh
bash scripts/s6.sh
```

## Citation
If you find this repository useful in your research, please consider giving a star ⭐ and a citation

```bib
@inproceedings{graphssm,
author = {Jintang Li and
Ruofan Wu and
Xinzhou Jin and
Boqun Ma and
Liang Chen and
Zibin Zheng},
title = {State Space Models on Temporal Graphs: {A} First-Principles Study},
booktitle = {NeurIPS},
year = {2024}
}
```