Fast node ranking algorithms on large graphs.

Author: Emmanouil (Manios) Krasanakis
License: Apache 2.0

:hammer_and_wrench: Installation

This library requires Python 3.9 or later. Get the latest version per:

pip install --upgrade pygrank

Also install any of these optional dependencies to use the respective backend: tensorflow,pytorch,torch_sparse,matvec

:link: Documentation

https://pygrank.readthedocs.io

:brain: Overview

pygrank is a collection of node ranking algorithms and practices that support real-world conditions, such as large graphs and heterogeneous preprocessing and postprocessing requirements. Thus, it provides ready-to-use tools that simplify the deployment of theoretical advancements and testing of new algorithms.

:thumbsup: Contributing

Feel free to contribute in any way, for example through the issue tracker or by participating in [discussions](). Please check out the contribution guidelines to bring modifications to the code base. If so, make sure to follow the pull checklist described in the guidelines.

:notebook: Citation

If pygrank has been useful in your research and you would like to cite it in a scientific publication, please refer to the following paper:

@article{krasanakis2022pygrank,
  author       = {Emmanouil Krasanakis, Symeon Papadopoulos, Ioannis Kompatsiaris, Andreas Symeonidis},
  title        = {pygrank: A Python Package for Graph Node Ranking},
  journal      = {SoftwareX},
  year         = 2022,
  month        = oct,
  doi          = {10.1016/j.softx.2022.101227},
  url          = {https://doi.org/10.1016/j.softx.2022.101227}
}

To publish research that makes use of provided implementations, please cite their relevant publications.