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MA-Net: A Reliable Memetic Algorithm for Community Detection by Modularity Optimization

  • Conference paper
Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 1))

Abstract

The information that can be transformed in knowledge from data in challenging real-world problems follows the accelerated rate of the advancement of technology in many different fields from biology to sociology. Complex networks are a useful representation of many problems in these domains One of the most important and challenging problems in network analysis lies in detecting community structures. This area of algorithmic research has attracted great attention due to its possible application in many fields. In this study we propose the MA-Net, memetic algorithm to detect communities in network by optimizing modularity value which is fast and reliable in the sense that it consistently produces sound solutions. Experiments using well-known real-world benchmark networks indicate that in comparison with other state-of-the-art algorithms, MA-Net has an outstanding performance on detecting communities.

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Author information

Authors and Affiliations

  1. The Priority Research Centre in Bioinformatics, Biomarker Discovery and Information-based Medicine, Hunter Medical Research Institute, Newcastle, Australia

    Leila Moslemi Naeni, Regina Berretta & Pablo Moscato

  2. School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, Australia

    Leila Moslemi Naeni, Regina Berretta & Pablo Moscato

Authors
  1. Leila Moslemi Naeni
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  2. Regina Berretta
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  3. Pablo Moscato
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Corresponding author

Correspondence to Leila Moslemi Naeni .

Editor information

Editors and Affiliations

  1. Department of Infomatics, Kindai University, Faculty of Science and Technology, Higashi-Osaka, Japan

    Hisashi Handa

  2. Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan

    Hisao Ishibuchi

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Yew-Soon Ong

  4. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Kay Chen Tan

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Naeni, L.M., Berretta, R., Moscato, P. (2015). MA-Net: A Reliable Memetic Algorithm for Community Detection by Modularity Optimization. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_25

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  • DOI: https://doi.org/10.1007/978-3-319-13359-1_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13358-4

  • Online ISBN: 978-3-319-13359-1

  • eBook Packages: EngineeringEngineering (R0)

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