https://github.com/akhilpandey95/gnnnas
Work as part of ANL summer 2022 research with emphasis on utilizing symbolic programming to perform NAS on graph neural networks
https://github.com/akhilpandey95/gnnnas
graph-neural-networks graph-representation-learning neural-architecture-search representation-learning symbolic-programming
Last synced: 8 months ago
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Work as part of ANL summer 2022 research with emphasis on utilizing symbolic programming to perform NAS on graph neural networks
- Host: GitHub
- URL: https://github.com/akhilpandey95/gnnnas
- Owner: akhilpandey95
- License: mit
- Created: 2022-05-16T14:45:13.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-10-24T01:17:26.000Z (over 2 years ago)
- Last Synced: 2025-01-29T11:02:56.902Z (over 1 year ago)
- Topics: graph-neural-networks, graph-representation-learning, neural-architecture-search, representation-learning, symbolic-programming
- Language: Python
- Homepage: https://akhilpandey95.github.io/gnnNAS/
- Size: 96.7 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS.md
Awesome Lists containing this project
README
Library to write symbolic programs to generate expressive message passing neural networks
kgforge Documentation
What is it? •
Features •
Installation •
Usage •
Contributing
## What is it?
`kgforge` is a library which automates the generation of knowledge graphs from scholarly text.
## Features:
- **TODO**: TODO: Description.
## Installation:
### Poetry
```bash
poetry add kgforge
```
### Pip
```bash
pip install kgforge
```
Setup your local environment:
Any necessary environment variables description:
```shell
export SAMPLE_ENV_VARIABLE=${VALUE}
```
## Usage
Now that `kgforge` is installed, you're ready to start using it!
It's time to point you to the official [Documentation Website](https://akhilpandey95.github.io/gnnNAS/) for more information on how to use `kgforge`
## Contributing
If you'd like to contribute, be sure to check out our [contributing guide](./CONTRIBUTING.md)! If you'd like to work on any outstanding items, check out the `roadmap` section in the docs and get started :smiley:
Thanks goes to these incredible people.
