Graph Statistics in Python is a package for graph statistical algorithms.
A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.
The official documentation with usage is at https://graspy.neurodata.io/
Please visit the tutorial section in the official website for more in depth usage.
GraSPy package requires only a standard computer with enough RAM to support the in-memory operations.
This package is supported for Linux and macOS. The package has been tested on the following systems:
- Linux: Ubuntu 16.04
- macOS: Mojave (10.14.1)
- Windows: 10
This package is written for Python3. Currently, it is supported for Python 3.5, 3.6, and 3.7.
GraSPy mainly depends on the Python scientific stack.
networkx
numpy
pandas
scikit-learn
scipy
seaborn
pip install graspy
git clone https://github.com/neurodata/graspy
cd graspy
python3 setup.py install
We welcome contributions from anyone. Please see our contribution guidelines before making a pull request. Our issues page is full of places we could use help! If you have an idea for an improvement not listed there, please make an issue first so you can discuss with the developers.
This project is covered under the Apache 2.0 License.
We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our issues page if you have questions or ideas.