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Dynamic_CommunityDetection_Benchmark

This is the CoD\AE N framework to generate Dynamic LFR Graphs, presented in CoDÆN: Benchmarks and Comparison of Evolutionary Community Detection Algorithms for Dynamic Networks. If you use this framework in your work, please cite the following paper:

@article{Paoletti2025CoDAEN,
author = {Paoletti, Giordano and Gioacchini, Luca and Mellia, Marco and Vassio, Luca and Almeida, Jussara},
title = {CoD{\AE}N: Benchmarks and Comparison of Evolutionary Community Detection Algorithms for Dynamic Networks},
year = {2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1559-1131},
url = {https://doi.org/10.1145/3718988},
doi = {10.1145/3718988},
note = {Just Accepted},
journal = {ACM Trans. Web},
month = feb,
keywords = {Community Detection, Dynamic Networks, Online Networks, Modularity, Benchmark}
}

Folder Structure

  • ./experiments: This folder contains all the scripts used to reproduce the synthetic experiments conducted for the paper. You can run these scripts to replicate the results discussed in the publication.

  • ./src: This folder contains the full source code of the proposed benchmark.

  • ./results: This folder is used to store the results by default.

  • ./notebook: This folder contains an example guide on how to use the framework. It provides a step-by-step explanation to help you get started with utilizing CoD\AE N in your own research.

Feel free to explore and adapt the provided scripts for your own experiments. If you have any questions, please reach out to us.