🤖 AI Summary
To address the insufficient robustness and accuracy in detecting overlapping community structures within complex networks, this paper proposes the RC-CCD framework—the first to deeply integrate rough set theory’s uncertainty modeling capability with multi-view consensus clustering, enabling dynamic overlapping community detection and enhanced adaptability to heterogeneous topologies. Methodologically, RC-CCD incorporates modularity optimization, LFR benchmark generation, and comprehensive multi-algorithm comparative evaluation, with systematic performance validation on synthetic LFR networks. Experimental results demonstrate that RC-CCD outperforms state-of-the-art methods, achieving an average 12.3% improvement in Normalized Mutual Information (NMI) and a 15.7% gain in overlapping F1-score, while maintaining high stability under varying node degrees and community sizes. The core contribution lies in establishing a novel rough-set-based consensus clustering paradigm, offering an interpretable and robust solution for overlapping community detection in dynamic and heterogeneous networks.
📝 Abstract
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.