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A Comparative Analysis of Different Multilevel Approaches for Community Detection

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Metaheuristics (MIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13838))

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Abstract

Community Detection is one of the most investigated problems as it finds application in many real-life areas. However, detecting communities and analysing community structure are very computationally expensive tasks, especially on large networks. In light of this, to better manage large networks, two new Multi-Level models are proposed in order to reduced and simplify the original graph via aggregation of groups of nodes. Both models have been applied on two variants of an immune-inspired algorithm, the first one based on a fully random-search process, and the second based on a hybrid approach. From the experimental analysis it clearly appears that the two proposed models help the random-search and the hybrid immune-inspired algorithms to significantly improve their performances from both computational and quality of found solutions point of view. In particular, the hybrid variant appears to be very competitive and efficient.

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Notes

  1. 1.

    A user-defined parameter.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, v.le A. Doria 6, 95125, Catania, Italy

    Guido Bordonaro, Rocco A. Scollo, Vincenzo Cutello & Mario Pavone

Authors
  1. Guido Bordonaro
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  2. Rocco A. Scollo
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  3. Vincenzo Cutello
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  4. Mario Pavone
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Corresponding author

Correspondence to Rocco A. Scollo .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Luca Di Gaspero

  2. University of Napoli - Federico II, Napoli, Italy

    Paola Festa

  3. University of Paris Est, Paris, France

    Amir Nakib

  4. University of Catania, Catania, Italy

    Mario Pavone

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Bordonaro, G., Scollo, R.A., Cutello, V., Pavone, M. (2023). A Comparative Analysis of Different Multilevel Approaches for Community Detection. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_17

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  • DOI: https://doi.org/10.1007/978-3-031-26504-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26503-7

  • Online ISBN: 978-3-031-26504-4

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