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[NeurIPS 2024] Spiking Graph Neural Networks on Riemannian Manifolds

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[NeurIPS 2024] Spiking Graph Neural Networks on Riemannian Manifolds

Get Started

Install Python packages.

pip install -r requirements.txt

Then you can run the command to train the model.

python main.py --task NC --dataset Physics --root_path ${your_path}

please replace ${your_path} with your dataset file path. If you want to use vallina SNN neurons, you can add --use_MS.

If you need to use product space of manifolds, you can add --use_product.

All the configuration of models can be load from Json file in ./configs.

Model Architecture

./pics/model_1.png
./pics/model_2.png

Results

./pics/results.png

Visualization

./pics/Torus.png
Figure 1. Visualization of 34-th node on KarateClub dataset on Torus manifold.


./pics/manifold_0.png
Figure 2. Visualization of 1-th node on KarateClub dataset on Sphere manifold.


./pics/manifold_16.png
Figure 3. Visualization of 17-th node on KarateClub dataset on Sphere manifold.

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