Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)

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Readme

This repository implements Graph Neural Tangent Kernel (infinitely wide multi-layer GNNs trained by gradient descent), described in the following paper:

Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu. Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS 2019. [arXiv] [Paper]

Unzip the dataset file

```
unzip dataset.zip
```

Here we demonstrate how to use GNTK to perform classification on IMDB-BINARY dataset. We set the number of BLOCK operations to be 2, the number of MLP layers to be 2 and c_u to be 1.

Compute the GNTK gram matrix

```
mkdir out
python gram.py --dataset IMDBBINARY --num_mlp_layers 2 --num_layers 2 --scale uniform --jk 1 --out_dir out
```

Classification with kernel regression

```
python search.py --data_dir ./out --dataset IMDBBINARY
```

Therefore we get the hyper-parameter search results at `./out/grid_search.csv`

.

To run the experiment described in our paper, please run `bash run_gram.sh`

and `bash run_search.sh`

in order.

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