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Subgraph Counts in Random Clustering Graphs

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Modelling and Mining Networks (WAW 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14671))

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Abstract

We analyze subgraph counts in random clustering graphs for general degree distributions. Building on the prior work, we weaken the assumptions required to derive our previous results and exactly determine the asymptotics of subgraph counts in a random clustering graphs under mild conditions. As an application, we analyze the clustering coefficient and cycle counts in random clustering graphs.

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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. SGH Warsaw School of Economics, Warsaw, Poland

    Bogumił Kamiński

  3. SGH Warsaw School of Economics, Warsaw, Poland

    Daniel Kaszyński

  4. SGH Warsaw School of Economics, Warsaw, Poland

    Łukasz Kraiński

  5. Toronto Metropolitan University, Toronto, ON, Canada

    Paweł Prałat

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

    François Théberge

  7. SGH Warsaw School of Economics, Warsaw, Poland

    Małgorzata Wrzosek

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Chung, F., Sieger, N. (2024). Subgraph Counts in Random Clustering Graphs. In: Dewar, M., et al. Modelling and Mining Networks. WAW 2024. Lecture Notes in Computer Science, vol 14671. Springer, Cham. https://doi.org/10.1007/978-3-031-59205-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-59205-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-59204-1

  • Online ISBN: 978-3-031-59205-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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