GraphKAN -- Implementation of Graph Neural Network version of Kolmogorov Arnold Networks (GraphKAN)

The only dependencies are torch and pytorch_geometric.

If the gpu (cuda) running fails, change to cpu training by changing:

args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ------> args.device = torch.device('cpu')

Thanks to the original implementations KAN (https://github.com/KindXiaoming/pykan) and FourierKAN (https://github.com/GistNoesis/FourierKAN), you guys are amazing.

Still at experimental stage to see if KAN really works on graph-structured data.

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Two key experimental observations:

(1) It is way much better to implment KAN for graphs in the latent feature space.

That is having a Linear Layer to project the input feature in to latent space first: self.lin_in = nn.Linear(in_feat, hidden_feat, bias=use_bias)

Rather than directly using the KAN Layer for input features like: self.lin_in = KANLayer(in_feat, hidden_feat, grid_feat, addbias=use_bias)

! ! ! If you do not have the Linear Layer for low-dimensional latent feature projection, KAN WILL NOT HAVE ANY TRAINING SIGNAL (this is really interesting, expect to work on some theoretical aspects after obtaining some experimental results)

Dont know if the trick works for images, languages, etc.

(2) Using SGD (ASGD) optimizer is way more stable than using Adam (AdamW) optimizer, but notice SDG is really slow to converage,

e.g., on Cora, Adam takes ~200 epochs, SGD takes ~10000 epochs.