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

Science Score: 31.0%

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Low similarity (11.8%) to scientific vocabulary

Last synced: 11 months ago · JSON representation ·

Repository

[IEEE ICMLA '23] Semi-supervised learning with dimensionality reduction and graph neural networks.

Basic Info

Host: GitHub
Owner: amorehead
License: mit
Language: Python
Default Branch: main
Homepage:
Size: 218 KB

Statistics

Stars: 3
Watchers: 4
Forks: 0
Open Issues: 0
Releases: 0

Created over 4 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Citation

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 littrainmodel.py

Or, plot dimensionality reduction model results

python3 litrundim_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} }

Owner

Name: Alex Morehead
Login: amorehead
Kind: user
Location: Columbia, Missouri
Company: University of Missouri

Website: https://amorehead.github.io/
Twitter: MoreheadAlex
Repositories: 74
Profile: https://github.com/amorehead

Machine learning & computational biology PhD @ University of Missouri. Prev: Research intern @ Profluent & Absci. Geometric deep learning & generative models.

Citation (citation.bib)

@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}
}

GitHub Events

Total

Last Year

Committers

Last synced: over 1 year ago

All Time

Total Commits: 41
Total Committers: 2
Avg Commits per committer: 20.5
Development Distribution Score (DDS): 0.415

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
tmwatchanan	t**n@g**m	24
Alex Morehead	a**d@g**m	17

Issues and Pull Requests

Last synced: over 1 year ago

All Time

Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

ssl-with-dr-and-gnns

Science Score: 31.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

SSL-With-DR-And-GNNs

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

Clone this repository:

Change to project directory:

Set up Conda environment locally

Activate Conda environment located in the current directory:

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

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

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

Run the PyTorch Lightning model training script:

Or, plot dimensionality reduction model results

Citing this work

Owner

Citation (citation.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

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Issue Labels

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