Random Network Null Models

Random Network Null Models is a methodological construct that specifies a class of synthetic networks generated by randomizing certain properties of an observed network while preserving others, serving to distinguish non-random structural features such as modularity or network motifs from those that could arise by chance under a given null hypothesis.

45

Publications

3.4K

Citations

116

Authors

92

Institutions

Publications per year

2017–2026

12

Authors

116

  1. SB
    Shweta Bansal

    Georgetown University, United States

    4 publications · 269 citations

    4

    h-index

  2. RP
    Romualdo Pastor‐Satorras

    Universitat Politècnica de Catalunya, Spain

    2 publications · 859 citations

    2

    h-index

  3. RJ
    Raúl J. Mondragón

    Queen Mary University of London, United Kingdom

    2 publications · 82 citations

    2

    h-index

  4. SK
    Shashank Khandelwal

    Pennsylvania State University, United States

    2 publications · 171 citations

    2

    h-index

  5. NP
    Nataša Pržulj

    Imperial College London, United Kingdom

    2 publications · 168 citations

    2

    h-index

1–5 of 116

Venues

  1. Physical Review E

    PRE · 9 publications · 1.6K citations

    9

    h-index

  2. Journal of Complex Networks

    3 publications · 55 citations

    3

    h-index

  3. Physical Review Letters

    PRL · 3 publications · 118 citations

    3

    h-index

  4. BMC Bioinformatics

    2 publications · 193 citations

    2

    h-index

  5. Behavioral Ecology and Sociobiology

    2 publications · 70 citations

    2

    h-index

1–5