https://github.com/skeletondyh/GRED

[ICML 2024] Recurrent Distance Filtering for Graph Representation Learning
https://github.com/skeletondyh/GRED

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[ICML 2024] Recurrent Distance Filtering for Graph Representation Learning

• Host: GitHub
• URL: https://github.com/skeletondyh/GRED
• Owner: skeletondyh
• Created: 2023-12-01T12:33:52.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-10T10:49:28.000Z (7 months ago)
• Last Synced: 2024-08-01T04:02:09.825Z (6 months ago)
• Language: Python
• Homepage:
• Size: 15.6 KB
• Stars: 6
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# GRED

This repo contains the official code for our ICML 2024 paper: **Recurrent Distance Filtering for Graph Representation Learning** (https://arxiv.org/abs/2312.01538)

## Setup

The code is developed using JAX and Flax (since we want to use `associative_scan`):

```

python==3.10.12

jaxlib==0.4.14+cuda11.cudnn86

flax==0.7.2

optax==0.1.7   # optimizer lib

numpy==1.25.2

scipy==1.11.2

scikit-learn==1.3.0

```

Please refer to the [JAX](https://jax.readthedocs.io/en/latest/installation.html) and [Flax](https://github.com/google/flax?tab=readme-ov-file#quick-install) installation pages.

To keep the data preprocessing consistent with other baselines, we load the datasets using `torch-geometric==2.3.1` and convert them into NumPy arrays.

You need `torch-geometric` to run `preprocess.py` and `preprocess_peptides.py`, but you don't need it to run training scripts.

## Data Preprocessing

To prepare MNIST, CIFAR10, ZINC, PATTERN, and CLUSTER, please run:

```

python preprocess.py

```

To prepare Peptides-func and Peptides-struct, please run:

```

python preprocess_peptides.py

```

## Training

For MNIST and CIFAR10:

```

python train_pixel.py --name MNIST --num_layers 4 --num_hops 3 --dim_h 128 --dim_v 96

python train_pixel.py --name CIFAR10 --num_layers 8 --num_hops 5 --dim_h 96 --dim_v 64

```

For ZINC:

```

python train_zinc.py

```

For CLUSTER and PATTERN:

```

python train_sbm.py --name CLUSTER --num_layers 16 --dim_h 64 --dim_v 64 --weight_decay 0.2 --r_min 0.9

python train_sbm.py --name PATTERN --num_layers 10 --dim_h 72 --dim_v 64 --weight_decay 0.1 --r_min 0.5

```

For Peptides-func and Peptides-struct:

```

python train_peptides_func.py

python train_peptides_struct.py

```

---

If you have any questions regarding the code, please feel free to raise an issue.

If you find our paper helpful in your research, please consider citing it:

```

@inproceedings{ding2024gred,

title={Recurrent Distance Filtering for Graph Representation Learning},

author={Yuhui Ding and Antonio Orvieto and Bobby He and Thomas Hofmann},

booktitle={Forty-first International Conference on Machine Learning},

year={2024}

}

```