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A Random Graph Model for Clustering Graphs

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Algorithms and Models for the Web Graph (WAW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13894))

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Abstract

We introduce a random graph model for clustering graphs with a given degree sequence. Unlike previous random graph models, we incorporate clustering effects into the model without any geometric conditions. We show that random clustering graphs can yield graphs with a power-law expected degree sequence, small diameter, and any desired clustering coefficient. Our results follow from a general theorem on subgraph counts which may be of independent interest.

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Acknowledgments

We thank the anonymous referees for their thorough reviews and invaluable suggestions.

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Authors and Affiliations

  1. University of California, San Diego, La Jolla, CA, 92037, USA

    Fan Chung & Nicholas Sieger

Authors
  1. Fan Chung
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  2. Nicholas Sieger
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Corresponding author

Correspondence to Nicholas Sieger .

Editor information

Editors and Affiliations

  1. Tutte Institute for Mathematics and Computing, Ottawa, ON, Canada

    Megan Dewar

  2. Toronto Metropolitan University, Toronto, ON, Canada

    Paweł Prałat

  3. SGH Warsaw School of Economics, Warsaw, Poland

    Przemysław Szufel

  4. Tutte Institute for Mathematics and Computing, Ottawa, ON, Canada

    François Théberge

  5. SGH Warsaw School of Economics, Warsaw, Poland

    Małgorzata Wrzosek

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Chung, F., Sieger, N. (2023). A Random Graph Model for Clustering Graphs. In: Dewar, M., Prałat, P., Szufel, P., Théberge, F., Wrzosek, M. (eds) Algorithms and Models for the Web Graph. WAW 2023. Lecture Notes in Computer Science, vol 13894. Springer, Cham. https://doi.org/10.1007/978-3-031-32296-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-32296-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32295-2

  • Online ISBN: 978-3-031-32296-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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