Jintang Li1, Ruofan Wu2, Boqun Ma3, Xinzhou Jin1, Liang Chen1, Zibin Zheng1
1Sun Yat-sen University, 2Coupang, 3Shanghai Jiao Tong University
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 bash scripts/s4.sh bash scripts/s5.sh bash scripts/s6.sh If you find this repository useful in your research, please consider giving a star ⭐ and a citation
@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} }