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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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Correspondence to Nicholas Sieger .

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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

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