GAV

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A link prediction algorithm tailored to flow-driven spatial networks. Paper accepted @ WACV24.

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# Graph Attentive Vectors (GAV) [![WACV](https://img.shields.io/badge/WACV-Link_Prediction_for_Flow_Driven_Spatial_Networks-blue.svg)](https://openaccess.thecvf.com/content/WACV2024/html/Wittmann_Link_Prediction_for_Flow-Driven_Spatial_Networks_WACV_2024_paper.html)

To run our proposed GAV framework on the ogbl-vessel benchmark, please follow the instructions below. The ogbl-vessel benchmark's data will be automatically downloaded and stored under `./dataset`.
For details, please check our paper and its extensive supplementary material.

## News
**January 24**: [*Link Prediction for Flow-Driven Spatial Networks*](https://openaccess.thecvf.com/content/WACV2024/html/Wittmann_Link_Prediction_for_Flow-Driven_Spatial_Networks_WACV_2024_paper.html) has been accepted and presented at WACV!

## Installation

Please create a new virtual environment using, e.g., anaconda:

conda create --name gav python=3.8.15

Subsequently, install the required packages:

pip install torch==1.13.1 --extra-index-url https://download.pytorch.org/whl/cu116

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv torch_geometric -f https://data.pyg.org/whl/torch-1.13.0+cu116.html

pip install ogb tensorboard tqdm networkit

The installation was tested using Ubuntu 16.04 and CUDA 11.6.

## Training

To train GAV, please run:

python gav_link_pred.py --save_appendix <appendix> --gpu_id <gpu_id> --dataset ogbl-vessel

Checkpoints and tensorboard log files will be stored under `./results`.

## Testing

To test GAV's performance on an individual checkpoint, please run:

python gav_link_pred.py --save_appendix <appendix_of_run_of_ckpt> --gpu_id <gpu_id> --dataset ogbl-vessel --only_test --continue_from <ckpt_epoch_nr>

To test GAV's performance on our provided checkpoint, please run:

python gav_link_pred.py --save_appendix _gav --gpu_id <gpu_id> --dataset ogbl-vessel --only_test --continue_from 34

## Road Networks
### Preprocessing

To preprocess the road network datasets, please download the graph and coordinates from [here](https://www.cc.gatech.edu/dimacs10/archive/streets.shtml). The downloaded file from **Graph** should be called `edges.graph`, while the downloaded file from **Coordinates** should be called `nodes.graph`. Both files should be stored in an individual directory located at `<path_to_downloaded_files>`. Finally, run:

python create_dataset.py --path <path_to_downloaded_files> --gpu_id <gpu_id> --dataset_name <e.g., ogbl-luxembourg_road>

Please note that in the preprocessing step, the `--dataset_name` has to start with `ogbl-` and should not include additional hyphens.

### Training and Testing

Follow the instructions above and simply state the processed dataset's name after the `--dataset` flag, omitting `ogbl-`. E.g., `--dataset luxembourg_road`.

## More Whole-Brain Vessel Graphs
### Preprocessing

To preprocess additional whole-brain vessel graphs, please download the **raw** data from [here](https://github.com/jocpae/VesselGraph).
The downloaded files should be stored in an individual directory located at `<path_to_downloaded_files>` Finally, run:

python create_dataset.py --path <path_to_downloaded_files> --gpu_id <gpu_id> --dataset_name <e.g., ogbl-c57_tc_vessel>

Please note that in the preprocessing step, the `--dataset_name` has to start with `ogbl-` and should not include additional hyphens.

### Training and Testing

Follow the instructions above and simply state the processed dataset's name after the `--dataset` flag, omitting `ogbl-`. E.g., `--dataset c57_tc_vessel`.

## Citation
If you find our work useful for your research, please consider citing:
```bibtex
@InProceedings{Wittmann_2024_WACV,
    author    = {Wittmann, Bastian and Paetzold, Johannes C. and Prabhakar, Chinmay and Rueckert, Daniel and Menze, Bjoern},
    title     = {Link Prediction for Flow-Driven Spatial Networks},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {2472-2481}
}
```

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