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Community Detection in Bipartite Networks Using a Noisy Extremal Optimization Algorithm

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

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Abstract

The network community structure detection problem consists in finding groups of nodes that are more connected to each other than to the rest of the network. While many methods have been designed to deal with this problem for general networks, there are few methods that deal with bipartite ones. In this paper we explore the behavior of an optimization method designed for identifying the community structure of unweighted networks when dealing with bipartite networks. We find that by using the specific Barber modularity, results are comparable with those reported in literature.

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Acknowledgments

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-2332.

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Authors and Affiliations

  1. Babeş-Bolyai University, Cluj, Romania

    Noémi Gaskó, Rodica Ioana Lung & Mihai Alexandru Suciu

Authors
  1. Noémi Gaskó
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  2. Rodica Ioana Lung
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  3. Mihai Alexandru Suciu
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Corresponding author

Correspondence to Noémi Gaskó .

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Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Gaskó, N., Lung, R.I., Suciu, M.A. (2017). Community Detection in Bipartite Networks Using a Noisy Extremal Optimization Algorithm. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_86

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_86

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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