A High-Performance Evolutionary Multiobjective Community Detection Algorithm
Guilherme O. Santos, Lucas S. Vieira, Giulio Rossetti, Carlos H. G. Ferreira, Gladston Moreira

TL;DR
The paper introduces HP-MOCD, a high-performance multi-objective evolutionary algorithm for community detection in large networks, achieving scalability, efficiency, and high-quality results with Pareto optimal solutions.
Contribution
It presents HP-MOCD, the first scalable, parallelized MOEA for large-scale community detection, with topology-aware operators and an open-source Python library.
Findings
Processes networks with over 1 million nodes efficiently.
Outperforms other MOEAs by over 531 times in runtime.
Capable of analyzing large real-world networks within reasonable time.
Abstract
Community detection in complex networks is fundamental across social, biological, and technological domains. While traditional single-objective methods like Louvain and Leiden are computationally efficient, they suffer from resolution bias and structural degeneracy. Multi-objective evolutionary algorithms (MOEAs) address these limitations by simultaneously optimizing conflicting structural criteria, however, their high computational costs have historically limited their application to small networks. We present HP-MOCD, a High-Performance Evolutionary Multiobjective Community Detection Algorithm built on Non-dominated Sorting Genetic Algorithm II (NSGA-II), which overcomes these barriers through topology-aware genetic operators, full parallelization, and bit-level optimizations, achieving theoretical O(GN_p|V|) complexity. We conduct experiments on both synthetic and real-world…
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