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An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks

Author

Listed:
  • Feifan Wang
  • Baihai Zhang
  • Senchun Chai
  • Yuanqing Xia

Abstract

Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the -means clustering techniques, we propose a novel community detection method that surpasses traditional -means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.

Suggested Citation

  • Feifan Wang & Baihai Zhang & Senchun Chai & Yuanqing Xia, 2018. "An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-10, August.
    • Handle: RePEc:hin:complx:8098325
    DOI: 10.1155/2018/8098325
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    References listed on IDEAS

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    1. Antonios Garas & Athanasios Lapatinas & Konstantinos Poulios, 2016. "The Relation Between Migration And Fdi In The Oecd From A Complex Network Perspective," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(06n07), pages 1-20, September.
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    Cited by:

    1. Xiangyue Wang & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-14, January.

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