A Comprehensive Survey on Community Detection With Deep Learning

XSXing SuSXShan XueFLFanzhen LiuJWJia WuJYJian Yang

Macquarie University · Chinese Academy of Sciences · +5 more institutions

PubMed
Indexed inarxivcrossrefpubmed

Abstract

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years-particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods,…

Citation impact

326
total citations
FWCI
36.78
Percentile
100%
References
185
Citations per year

Authors

12
  • XS
    Xing SuCorresponding

    Macquarie University

  • SX
    Shan Xue

    Macquarie University

  • FL
    Fanzhen Liu

    Macquarie University

  • JW
    Jia Wu

    Macquarie University

  • JY
    Jian Yang

    Macquarie University

Topics & keywords

Keywords
  • Deep learning
  • Inference
  • Convolutional neural network
  • Deep belief network
  • Benchmark (surveying)
  • Cluster analysis
  • Adversarial system
  • Artificial neural network
No related works found for this paper.

Funding