https://github.com/amorehead/ssl-with-dr-and-gnns

[IEEE ICMLA '23] Semi-supervised learning with dimensionality reduction and graph neural networks.
https://github.com/amorehead/ssl-with-dr-and-gnns

Last synced: 13 days ago
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[IEEE ICMLA '23] Semi-supervised learning with dimensionality reduction and graph neural networks.

• Host: GitHub
• URL: https://github.com/amorehead/ssl-with-dr-and-gnns
• Owner: amorehead
• License: mit
• Created: 2021-11-03T23:32:20.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-02-09T00:56:09.000Z (9 months ago)
• Last Synced: 2024-05-15T09:51:59.307Z (6 months ago)
• Language: Python
• Homepage:
• Size: 218 KB
• Stars: 3
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: citation.bib

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README

        
# SSL-With-DR-And-GNNs

Semi-supervised learning with dimensionality reduction and graph neural networks.

## Setting Up Project via a Traditional Installation (for Linux-Based Operating Systems)

First, install and configure Conda environment:

```bash

# Clone this repository:

git clone https://github.com/amorehead/SSL-With-DR-And-GNNs

# Change to project directory:

cd SSL-With-DR-And-GNNs

# Set up Conda environment locally

conda env create --name DR_SSL -f environment.yml

# Activate Conda environment located in the current directory:

conda activate DR_SSL

# (Optional) Perform a full install of the pip dependencies described in 'requirements.txt':

pip3 install -r requirements.txt

# (Optional) To remove the long Conda environment prefix in your shell prompt, modify the env_prompt setting in your .condarc file with:

conda config --set env_prompt '({name})'

 ```

## Running Project after Performing a Traditional Installation (for Linux-Based Operating Systems)

Run like typical Python scripts:

```bash

# Run the PyTorch Lightning model training script:

python3 lit_train_model.py

# Or, plot dimensionality reduction model results

python3 lit_run_dim_red.py

 ```

## Citing this work

If you use the code associated with this repository or otherwise find this work useful, please cite:

```bibtex

@inproceedings{morehead2023ssl,

  title={Semi-Supervised Graph Learning Meets Dimensionality Reduction},

  author={Morehead, Alex and Chantapakul, Watchanan and Cheng, Jianlin},

  booktitle={IEEE International Conference on Machine Learning and Applications},

  year={2023}

}

```