Modularity Significance Testing

Modularity Significance Testing is a statistical method used in network science to assess whether the modularity value of a detected community structure significantly exceeds the expected modularity of a null model, thereby distinguishing meaningful community organization from random fluctuations in network topology.

8

Publications

339

Citations

12

Authors

9

Institutions

Publications per year

2017–2026

3

Authors

12

  1. JP
    James P. Bagrow

    University of Vermont, United States

    1 publications · 73 citations

    1

    h-index

  2. ZD
    Zhishan Dong

    Jilin University, China

    1 publications · 15 citations

    1

    h-index

  3. YC
    Yuguo Chen

    University of Illinois Urbana-Champaign, United States

    1 publications · 10 citations

    1

    h-index

  4. QL
    Qun Liu

    Ocean University of China, China

    1 publications · 15 citations

    1

    h-index

  5. JH
    Justin H. Kirkland

    University of Houston, United States

    1 publications · 18 citations

    1

    h-index

1–5 of 12

Venues

  1. Scientific Reports

    2 publications · 167 citations

    2

    h-index

  2. Trends in Ecology & Evolution

    TREE · 1 publications · 34 citations

    1

    h-index

  3. Statistica Sinica

    1 publications · 10 citations

    1

    h-index

  4. Physical Review E

    PRE · 1 publications · 73 citations

    1

    h-index

  5. Bristol Research (University of Bristol)

    Repository · 1 publications · 22 citations

    1

    h-index

1–5