https://github.com/facebookresearch/forwardgnn

This repository provides code for the paper "Forward Learning of Graph Neural Networks" (ICLR 2024).
https://github.com/facebookresearch/forwardgnn

forward-learning graph-learning graph-neural-networks link-prediction node-classification

Last synced: 10 months ago
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This repository provides code for the paper "Forward Learning of Graph Neural Networks" (ICLR 2024).

• Host: GitHub
• URL: https://github.com/facebookresearch/forwardgnn
• Owner: facebookresearch
• License: other
• Created: 2024-03-14T17:46:40.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-18T17:43:51.000Z (almost 2 years ago)
• Last Synced: 2025-03-29T07:05:40.766Z (11 months ago)
• Topics: forward-learning, graph-learning, graph-neural-networks, link-prediction, node-classification
• Language: Python
• Homepage:
• Size: 425 KB
• Stars: 17
• Watchers: 1
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

          
# Forward Learning of Graph Neural Networks

This repository provides the code of the ForwardGNN framework presented in the paper ["Forward Learning of Graph Neural Networks"](https://arxiv.org/pdf/2403.11004.pdf), 

Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed, 

The Twelfth International Conference on Learning Representations (ICLR) 2024.







***Learning graph neural networks (GNNs) with (a) backpropagation and (b)-(d) the proposed forward learning approaches of the ForwardGNN framework**:

(a) Backpropagation involves one forward pass, followed by one backward pass through the network. 

(b) The forward-forward approach involves two forward passes on positive and negative inputs. 

(c) The single-forward approach learns via just one forward pass. 

(d) The single-forward approach is extended to incorporate top-down signals.*

## Installation

Running [install_packages.sh](install/install_packages.sh) sets up the conda environment named `ForwardLearningGNN` and installs required packages.

## Datasets

Datasets used in the paper will be downloaded in the [`data`](./data/) folder when the code runs for the first time.

## Data Splits

Node and edge splits to be used for node classification and link prediction, respectively, 

will be generated in the [`datasplits`](./datasplits/) folder when the code runs for the first time.

To use the node and edge splits used for the experiments in the paper,

download them from 

[this repository](https://github.com/NamyongPark/forwardgnn-datasplits), and 

place them in the [`datasplits`](./datasplits/) folder.

## How to Run

Scripts in [`exp/nodeclass/`](./exp/nodeclass/) and [`exp/linkpred/`](./exp/linkpred/) can be used 

to train GNNs using the proposed forward learning algorithms of ForwardGNN or backpropagation 

for node classification and link prediction, respectively.

## Directory Structure

* [`src/`](./src/): source code root directory

  * [`src/train_forward.py`](./src/train_forward.py): script to train GNNs using the forward learning algorithms

  * [`src/forward_learning`](./src/forward_learning): root package for the forward learning algorithms

    * [`src/forward_learning/nodeclass`](./src/forward_learning/nodeclass): package for variants of forward learning algorithms for node classification

    * [`src/forward_learning/linkpred`](./src/forward_learning/linkpred): package for variants of forward learning algorithms for link prediction

  * [`src/train_backprop.py`](./src/train_backprop.py): script to train GNNs using backpropagation

  * [`src/gnn`](./src/gnn): package with adapted GNN operators

  * [`src/datasets`](./src/datasets): package with scripts to preprocess and load data and data splits

  * [`src/utils`](./src/utils): package for various utility classes and functions, e.g., performance evaluation, early stopping, and logging.

* [`exp/`](./exp/): directory with scripts to train GNNs using the proposed forward learning algorithms and backpropagation

* [`data/`](./data/): directory to store graph data files

* [`datasplits/`](./datasplits/): directory to store graph data splits

## Citation

If you use code in this repository in your own work, please cite our paper.

```bibtex

@inproceedings{park2024forwardgnn,

  title={Forward Learning of Graph Neural Networks},

  author={Namyong Park and Xing Wang and Antoine Simoulin and Shuai Yang and Grey Yang and Ryan Rossi and Puja Trivedi and Nesreen Ahmed},

  booktitle={ICLR},

  year={2024},

}

```

## License

The majority of the ForwardGNN project is licensed under CC-BY-NC, 

however portions of the project are available under separate license terms.

The GCN and GraphSAGE operators in the [gnn](./src/gnn) folder adapted the PyG's implementation, 

which is licensed under the MIT license.

The forward learning algorithms in the [nodeclass](./src/forward_learning/nodeclass) and [linkpred](./src/forward_learning/linkpred) folders 

build upon the Nebuly's implementation of the forward-forward algorithm, 

which is licensed under the Apache-2.0 license.

## Contributing

See the [CONTRIBUTING](CONTRIBUTING.md) file for how to contribute to the project.