Citation

  @article{santos2025hpmocd,
  author = {Guilherme Oliveira Santos, Carlos H. G. Ferreira and Gladston J. P. Moreira},
  title = {HP-MOCD: A High-Performance Multi-Objective Community Detection Algorithm for Large-Scale Networks},
  year = {2025},
  abstract = {Community detection in social networks has traditionally been approached as a single-objective optimization problem, with various heuristics targeting specific community-defining metrics. However, this approach often proves inadequate for capturing the multifaceted nature of communities. We introduce HP-MOCD, a fully parallelized, evolutionary high-performance multi-objective community detection algorithm designed specifically for large-scale networks. Our implementation overcomes the computational challenges that typically limit multi-objective approaches in this domain. While performance may decrease with networks containing high proportions of inter-community edges, extensive evaluations on synthetic datasets demonstrate that HP-MOCD achieves an exceptional balance between scalability and detection accuracy. Available as open-source software, HP-MOCD offers researchers and practitioners a practical, powerful solution for complex network analysis, particularly for applications requiring both efficiency and detection quality.},
  keywords = {community detection, complex networks, evolutionary algorithms, genetic algorithms, multi-objective}
}