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Exploring Multi-objective Cellular Genetic Algorithms in Community Detection Problems

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

Abstract

Interest in network analysis has not stopped increasing over the last decade. The Community Detection Problem (CDP) has been a hot topic in network analysis, so many different approaches have been proposed. Among them, optimization methods have proven to be highly effective for this task. Traditionally, the CDP has been tackled as a single-objective optimization problem. Nevertheless, this trend has started to change, and new methods have appeared following multi-objective approaches. Genetic Algorithms have been applied to the CDP with relative success, especially NSGA-II. However, cellular Genetic Algorithms (cGAs) have yet received little attention. In cGAs, the population is structured in small overlapping neighborhoods producing a slow spread of high-quality solutions. The main contribution of this paper is understanding if the smooth diffusion scheme of MoCell (a multi-objective cGA) can provide any benefit over current multi-objective GAs for the CDP. To verify the effectiveness of MoCell, an evaluation was conducted on 21 synthetically generated networks and two real-world ones. The experiments show that MoCell is able to outperform NSGA-II, especially in large networks scenarios.

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Notes

  1. 1.

    https://www.kaggle.com/apanizolledot/dancer-synthetic-dynamic-networks.

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Acknowledgments

This work was written as part of a research stay of M. Pedemonte at Universidad Politécnica de Madrid (funded by grants from ANII - MOV\(\_\)CA\(\_\)2019\(\_\)1\(\_\)156657 and CSIC, UDELAR). M. Pedemonte also acknowledge support from PEDECIBA Informática, ANII, and SNI. This work has also been supported by other research grants: Spanish Ministry of Science and Education under TIN2014-56494-C4-4-P grant (DeepBio), and Comunidad Autónoma de Madrid under P2018/TCS-4566 grant (CYNAMON).

Author information

Authors and Affiliations

  1. InCo, Universidad de la República, Montevideo, Uruguay

    Martín Pedemonte

  2. Departmento de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain

    Ángel Panizo-LLedot, Gema Bello-Orgaz & David Camacho

Authors
  1. Martín Pedemonte
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  2. Ángel Panizo-LLedot
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  3. Gema Bello-Orgaz
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  4. David Camacho
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Corresponding author

Correspondence to Martín Pedemonte .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Pedemonte, M., Panizo-LLedot, Á., Bello-Orgaz, G., Camacho, D. (2020). Exploring Multi-objective Cellular Genetic Algorithms in Community Detection Problems. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_22

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  • Online ISBN: 978-3-030-62365-4

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