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https://github.com/facebookresearch/GraphLog

API for accessing the GraphLog dataset
https://github.com/facebookresearch/GraphLog

Last synced: about 2 months ago
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API for accessing the GraphLog dataset

Host: GitHub
URL: https://github.com/facebookresearch/GraphLog
Owner: facebookresearch
License: other
Created: 2020-02-19T18:25:33.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2024-05-03T19:54:36.000Z (5 months ago)
Last Synced: 2024-06-08T05:34:17.492Z (4 months ago)
Language: Jupyter Notebook
Homepage:
Size: 4.26 MB
Stars: 89
Watchers: 15
Forks: 10
Open Issues: 5
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

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# GraphLog

API to interface with the GraphLog Dataset. GraphLog is a multi-purpose, multi-relational graph dataset built using rules grounded in first-order logic.

[Homepage](https://www.cs.mcgill.ca/~ksinha4/graphlog/) | [Paper](https://arxiv.org/abs/2003.06560) | [API Docs](https://graphlog.readthedocs.io/en/latest/) | [Dataset](https://drive.google.com/file/d/1s6oG_5Ul199puKAu67z3QrOAm943hGV2/view?usp=sharing)



### News

- Core Generator logic of GraphLog is now released in its own repository, [GLC](https://github.com/koustuvsinha/glc)! This repository will contain the specific instantiations of GLC which can be used to create various setups of GraphLog. Stay tuned for more updates!

### Installation

* Supported Python Version: 3.6, 3.7, 3.8

* Install PyTorch from https://pytorch.org/get-started/locally/

* Install pytorch-geometric (and other dependencies) from https://github.com/rusty1s/pytorch_geometric#installation. Make sure that cpu/cuda versions for pytorch and pytorch-geometric etc matches.

* `pip install graphlog`

### QuickStart

Check out the notebooks on [Basic Usage](examples/Basic%20Usage.ipynb) and [Advanced Usage](examples/Advanced%20Usage.ipynb) to quickly start playing with GraphLog.

### Dev Setup

* `pip install -e ".[dev]"`

* Install pre-commit hooks `pre-commit install`

* The code is linted using:

    * `black`

    * `flake8`

    * `mypy`

* All the tests can be run locally using `nox`

### Experiments

Code for experiments used in our paper are available in `experiments/` folder.

### Questions

- If you have questions, open an Issue

- If you have any questions or topics related to this project to discuss, please contact `koustuvs[at]meta.com`.

### Contributing

Please open a Pull Request (PR).

### Citation

If our work is useful for your research, consider citing it using the following bibtex:

```

@article{sinha2020graphlog,

  Author = {Koustuv Sinha and Shagun Sodhani and Joelle Pineau and William L. Hamilton},

  Title = {Evaluating Logical Generalization in Graph Neural Networks},

  Year = {2020},

  arxiv = {https://arxiv.org/abs/2003.06560}

}

```

### License

CC-BY-NC 4.0 (Attr Non-Commercial Inter.)

### Terms of Use

https://opensource.facebook.com/legal/terms

### Privacy Policy

https://opensource.facebook.com/legal/privacy