https://github.com/neurodata/graph-stats-book
https://github.com/neurodata/graph-stats-book
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/neurodata/graph-stats-book
- Owner: neurodata
- License: gpl-3.0
- Created: 2020-09-15T18:56:59.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-05-23T13:11:15.000Z (about 3 years ago)
- Last Synced: 2023-06-07T16:31:20.183Z (about 3 years ago)
- Language: Jupyter Notebook
- Homepage: http://docs.neurodata.io/graph-stats-book/coverpage.html
- Size: 3.81 GB
- Stars: 20
- Watchers: 5
- Forks: 6
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Network Machine Learning in Python
This book provides an introduction to graph statistics, with a focus on useful representations of graphs and their applications on real data.
[Google Drive Brainstorm](https://drive.google.com/drive/folders/1mcMQWv0WfydQSdgchhla7XHj2PBPD-b9?usp=sharing)
[Book Proposal](https://docs.google.com/document/d/1VTlNaogB-WPyex9LYVh7PvmED9S8rNqHoTXlTre_ASA/edit?usp=sharing)
[Compiled Jupyter Book](http://docs.neurodata.io/graph-stats-book/)
## Usage
### Building the book
If you'd like to develop on and build the Network Machine Learning in Python book, you should:
- Clone this repository and run
- Run `pip install -r requirements.txt` (it is recommended you do this within a virtual environment)
- (Recommended) Remove the existing `network_machine_learning_in_python/_build/` directory
- Run `jupyter-book build network_machine_learning_in_python/`
A fully-rendered HTML version of the book will be built in `network_machine_learning_in_python/_build/html/index.html`.
### Hosting the book
The html version of the book is hosted on the `gh-pages` branch of this repo. A GitHub actions workflow has been created that automatically builds and pushes the book to this branch on a push or pull request to main.
If you wish to disable this automation, you may remove the GitHub actions workflow and build the book manually by:
- Navigating to your local build; and running,
- `ghp-import -n -p -f network_machine_learning_in_python/_build/html`
This will automatically push your build to the `gh-pages` branch. More information on this hosting process can be found [here](https://jupyterbook.org/publish/gh-pages.html#manually-host-your-book-with-github-pages).
## Contributors
We welcome and recognize all contributions. You can see a list of current contributors in the [contributors tab](https://github.com/jovo/network_machine_learning_in_python/graphs/contributors).
## Credits
This project is created using the excellent open source [Jupyter Book project](https://jupyterbook.org/) and the [executablebooks/cookiecutter-jupyter-book template](https://github.com/executablebooks/cookiecutter-jupyter-book).
## Color Schemes
- Sequential color scales: low `#fcfbfd`, high `#3f007d`, discrete [RColor Brewer Purples](https://colorbrewer2.org/#type=sequential&scheme=Purples&n=8)
- Divergent: low `#b2182b` middle `#f7f7f7` high `#2166ac`, discrete [RColor Brewer RdBu](https://colorbrewer2.org/#type=diverging&scheme=RdBu&n=9)
- Qualitative: [Matplotlib Tab10](https://matplotlib.org/stable/_images/sphx_glr_colormaps_006.png)
## Code
Functions specific to this book - e.g., plotting functions we use regularly - has been stored in the subpackage below.
https://github.com/neurodata/graphbook-code