pygrank 0.2.14

Recommendation algorithms for large graphs

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.