pymocd: multi-objective evolutionary community detection for NetworkX and igraph graphs, with a parallel Rust core.
pip install pymocd
Documentation: https://csilab.ufop.br/pymocd/
- Getting started and installation
- Algorithms: the library's methods and the re-implemented baselines, with their papers
- Evaluating partitions (NMI, AMI, ARI, F1) and LFR benchmark graphs
- Examples and the API reference
- Changelog, contributing and citing pymocd
Citation
If you use pymocd in your research, please cite:
Santos, G. O., Vieira, L. S., Rossetti, G., Ferreira, C. H. G., & Moreira, G. J. P. (2025). A high-performance evolutionary multiobjective community detection algorithm. Social Network Analysis and Mining, 15(1), 110. https://doi.org/10.1007/s13278-025-01519-7
@article{Santos2025,
author = {Santos, Guilherme O. and Vieira, Lucas S. and Rossetti, Giulio and Ferreira, Carlos H. G. and Moreira, Gladston J. P.},
title = {A high-performance evolutionary multiobjective community detection algorithm},
journal = {Social Network Analysis and Mining},
year = {2025},
volume = {15},
number = {1},
pages = {110},
doi = {10.1007/s13278-025-01519-7},
url = {https://doi.org/10.1007/s13278-025-01519-7},
issn = {1869-5469},
date = {2025-11-18}
}
For the re-implemented baselines, also cite their original papers; see Citing pymocd.
pymocd is licensed under the GPL-3.0-or-later.
1 maintainer
Metadata
1 maintainer
Release files for pymocd 6.1.1
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Source distribution (sdist)
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Built distributions (wheels)
Total release size: 13.8 MB
Release files / pymocd-6.1.1.tar.gz
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Release files / pymocd-6.1.1-cp313-cp313-win_amd64.whl
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