Graphkan: Implementation of Graph Neural Network version of Kolmogorov Arnold Networks with torch geometrics
In this repository, we have implemented Graphkan using pytorch_geometric. Therefore, you can easily apply GraphKan to your own graph tasks. We have tested GraphKan on signal classification tasks, and the results are as follows.
| Model | Set 1 | Set 2 | Set 3 | Set 4 |
|---|---|---|---|---|
| GCN | 0.9343 | 0.9457 | 0.8871 | 0.8186 |
| GCN with Kolmogorov Arnold Networks | 0.9643 | 0.9600 | 0.9214 | 0.8400 |
Learning curve on Set 1 is as below.
We can see that GraphKAN exhibits better expressive power in representation learning compared to original graph networks.
Training log is uploaded as PDF.
GCN: \log\graph.pdf
GCN with Kolmogorov Arnold Networks: \log\kangraph.pdf
If you want to use GraphKan on your own task, you can replace original ChebConv with the kanChebConv in model/GNNs.py.
(1) You can utilize various types of KAN, including FourierKan, ChebyKan and so on. However, it is unfortunate that FourierKAN and ChebyKAN are not effective for our tasks, and further investigation is needed to determine the cause.
(2) According to our experiments, the inclusion of LayerNorm or BatchNorm in your network may be necessary.
Thanks to the efficient-kan.