A Novel Algorithm for Community Detection in Networks using Rough Sets and Consensus Clustering
<|reference_start|>A Novel Algorithm for Community Detection in Networks using Rough Sets and Consensus Clustering: Complex networks, such as those in social, biological, and technological systems, often present challenges to the task of community detection. Our research introduces a novel rough clustering based consensus community framework (RC-CCD) for effective structure identification of network communities. The RC-CCD method employs rough set theory to handle uncertainties within data and utilizes a consensus clustering approach to aggregate multiple clustering results, enhancing the reliability and accuracy of community detection. This integration allows the RC-CCD to effectively manage overlapping communities, which are often present in complex networks.   This approach excels at detecting overlapping communities, offering a detailed and accurate representation of network structures. Comprehensive testing on benchmark networks generated by the Lancichinetti-Fortunato-Radicchi method showcased the strength and adaptability of the new proposal to varying node degrees and community sizes. Cross-comparisons of RC-CCD versus other well known detection algorithms outcomes highlighted its stability and adaptability.<|reference_end|>
@article{grass-boada2024a,
    title={A Novel Algorithm for Community Detection in Networks using Rough Sets
  and Consensus Clustering},
    author={Darian H. Grass-Boada, Leandro Gonz'alez-Montesino, Rub'en
  Arma~nanzas},
    journal={arXiv preprint arXiv:2406.12412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12412},
    primaryClass={cs.AI cs.SI}
}