2025: A High-Performance Evolutionary Multiobjective Community Detection Algorithm— Guilherme O. Santos, Lucas S. Vieira, Giulio Rossetti, Carlos H. G. Ferreira, Gladston Moreira.
Community structure is a key feature of complex networks, underpinning diverse phenomena acrosssocial, biological, and technological systems. While traditional methods like Louvain and Leiden offerefficient solutions, they rely on single-objective optimization, often failing to capture the multifacetednature of real-world networks. Multi-objective approaches address this limitation by consideringmultiple structural criteria simultaneously, but their high computational cost restricts their use in large-scale settings. We propose HP-MOCD, a high-performance, fully parallel evolutionary algorithmbased on NSGA-II, designed to uncover high-quality community structures by jointly optimizingconflicting objectives. HP-MOCD leverages topology-aware genetic operators and parallelism toefficiently explore the solution space and generate a diverse Pareto front of community partitions.Experimental results on large synthetic benchmarks demonstrate that HP-MOCD consistently outper-forms existing multi-objective methods in runtime, while achieving superior or comparable detectionaccuracy. These findings position HP-MOCD as a scalable and practical solution for communitydetection in large, complex networks.