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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2021: Intelligent Information and Database Systems pp 757–767Cite as

Community Detection in Complex Networks: A Survey on Local Approaches

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

Abstract

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient solution to deal with large-scale networks with millions to billions of nodes. This methodology has shifted the attention from global structure towards the local level to deal with a network using only a portion of nodes. Investigating the state-of-the-art, we notice the absence of a standard definition of locality between community detection algorithms. Different goals have been explored under the local terminology of community detection approaches that can be misunderstood. This paper probes existing contributions to extract the scopes where an algorithm performs locally. Our purpose is to interpret the concept of locality in community detection algorithms. We propose a locality exploration scheme to investigate the concept of locality at each stage of an existing community detection workflow. We summarized terminologies concerning the locality in the state-of-the-art community detection approaches. In some cases, we observe how different terms are used for the same concept. We demonstrate the applicability of our algorithm by providing a review of some algorithms using our proposed scheme. Our review highlights a research gap in community detection algorithms and initiates new research topics in this domain.

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Acknowledgment

This work has been partially funded by the joint research programme University of Luxembourg/SnT-ILNAS on Digital Trust for Smart-ICT.

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Authors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Saharnaz Dilmaghani, Matthias R. Brust, Gregoire Danoy & Pascal Bouvry

  2. Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Gregoire Danoy & Pascal Bouvry

Authors
  1. Saharnaz Dilmaghani
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  2. Matthias R. Brust
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  3. Gregoire Danoy
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  4. Pascal Bouvry
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Corresponding author

Correspondence to Saharnaz Dilmaghani .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Dilmaghani, S., Brust, M.R., Danoy, G., Bouvry, P. (2021). Community Detection in Complex Networks: A Survey on Local Approaches. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_60

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73279-0

  • Online ISBN: 978-3-030-73280-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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