Community Clustering based on Grey Wolf Optimization

Abstract

Community detection remains a fundamental challenge in network analysis, with critical applications across diverse domains, including social networks and biological systems. This paper introduces GreyWolf Optimization Clustering (GWOC) A novel approach that enhances the standard Grey Wolf Optimizer (GWO) by integrating hierarchical clustering mechanisms into its optimization process. Specifically,GWOC refines the encircling and attacking phases of the grey wolves’ hunting strategy by embedding a clustering-based local search that improves convergence precision and the delineation of community boundaries.Unlike traditional methods, GWOC employs an innovative three-phase strategy inspired by grey wolves’ hunting behavior: tracking, encircling, and attacking, achieving remarkable improvements in both detection accuracy and computational efficiency. Through extensive experimentation on networks, as well as synthetic Lancichinetti- Fortunato-Radicchi (LFR) networks, GWOC exhibits robustness under varying inter-community mixing levels, maintaining Normalized Mutual Information (NMI) scores above 0.9, ResMI scores exceeding 88 and Adjusted Rand Index (ARI) scores exceeding 0.92 in high-noise environments. The algorithm’s effectiveness is particularly notable in handling large-scale networks and maintaining high detection accuracy even with increasing inter-community mixing levels.