graspologicis 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://graspologic.readthedocs.io/en/latest/
Please visit the tutorial section in the official website for more in depth usage.
graspologic package requires only a standard computer with enough RAM to support the in-memory operations.
graspologic is tested on the following OSes:
And across the following versions of Python:
If you try to use
graspologic for a different platform than the ones listed and notice any unexpected behavior,
please feel free to raise an issue. It's better for ourselves and our users
if we have concrete examples of things not working!
pip install graspologic
git clone https://github.com/microsoft/graspologic cd graspologic python3 -m venv venv source venv/bin/activate 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 MIT 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.
If you find
graspologic useful in your work, please cite the package via the GraSPy paper
Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7.