HP-MOCD: A High-Performance Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks
DOI:
https://doi.org/10.5753/reic.2026.8488Keywords:
Community Detection, Multi-objective Evolutionary Algorithms, NSGA-II, Complex Networks, High-Performance ComputingAbstract
Community detection in complex networks is fundamental across social, biological, and technological domains. Traditional single-objective heuristics, such as Louvain and Leiden, are efficient but suffer from resolution bias and structural degeneracy. Multi-objective evolutionary algorithms (MOEAs) overcome these limitations by jointly optimizing conflicting criteria, yet their high computational cost has historically restricted them to small networks. This work introduces HP-MOCD, a high-performance MOEA built upon NSGA-II that combines topology-aware genetic operators, full parallelization, and bit-level optimizations, achieving O(G · Np|V |) complexity. Experiments on synthetic and real-world networks show superior accuracy across multiple scenarios and strong scalability: HP-MOCD processes graphs with over one million nodes while outperforming competing MOEAs by up to 553× in execution time. The algorithm is released as an open-source Python library.
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