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Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

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  • Vinícius da Fonseca Vieira
  • Carolina Ribeiro Xavier
  • Nelson Francisco Favilla Ebecken
  • Alexandre Gonçalves Evsukoff

Abstract

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

Suggested Citation

  • Vinícius da Fonseca Vieira & Carolina Ribeiro Xavier & Nelson Francisco Favilla Ebecken & Alexandre Gonçalves Evsukoff, 2014. "Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-15, December.
    • Handle: RePEc:hin:jnlmpe:502809
    DOI: 10.1155/2014/502809
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    Cited by:

    1. Beranek, L. & Remes, R., 2023. "The emergence of a core–periphery structure in evolving multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    2. Seungil Yum, 2023. "Information networks for COVID-19 according to race/ethnicity," Information Technology and Management, Springer, vol. 24(2), pages 147-157, June.
    3. del Barrio-García, Salvador & Muñoz-Leiva, Francisco & Golden, Linda, 2020. "A review of comparative advertising research 1975–2018: Thematic and citation analyses," Journal of Business Research, Elsevier, vol. 121(C), pages 73-84.
    4. Zuraida Abal Abas & Mohd Natashah Norizan & Zaheera Zainal Abidin & Ahmad Fadzli Nizam Abdul Rahman & Hidayah Rahmalan & Ida Hartina Ahmed Tharbe & Wan Farah Wani Wan Fakhruddin & Nurul Hafizah Mohd Z, 2022. "Modeling Physical Interaction and Understanding Peer Group Learning Dynamics: Graph Analytics Approach Perspective," Mathematics, MDPI, vol. 10(9), pages 1-18, April.

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