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Transience Versus Recurrence for Scale-Free Spatial Networks

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Algorithms and Models for the Web Graph (WAW 2020)

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Abstract

Weight-dependent random connection graphs are a class of local network models that combine scale-free degree distribution, small-world properties and clustering. In this paper we discuss recurrence or transience of these graphs, features that are relevant for the performance of search and information diffusion algorithms on the network.

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Acknowledgement

We acknowledge support from DFG through Scientific Network Stochastic Processes on Evolving Networks.

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Authors and Affiliations

  1. Mathematisches Institut, Universität zu Köln, Weyertal 86-90, 50931, Cologne, Germany

    Peter Gracar & Peter Mörters

  2. Mathematisches Institut, Ludwig-Maximilians-Universität München, Theresienstr. 39, 80333, Munich, Germany

    Markus Heydenreich

  3. Institut für Mathematik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55099, Mainz, Germany

    Christian Mönch

Authors
  1. Peter Gracar
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  2. Markus Heydenreich
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  3. Christian Mönch
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  4. Peter Mörters
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Corresponding author

Correspondence to Peter Gracar .

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Editors and Affiliations

  1. SGH Warsaw School of Economics, Warsaw, Poland

    Bogumił Kamiński

  2. Ryerson University, Toronto, ON, Canada

    Paweł Prałat

  3. SGH Warsaw School of Economics, Warsaw, Poland

    Przemysław Szufel

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Gracar, P., Heydenreich, M., Mönch, C., Mörters, P. (2020). Transience Versus Recurrence for Scale-Free Spatial Networks. In: Kamiński, B., Prałat, P., Szufel, P. (eds) Algorithms and Models for the Web Graph. WAW 2020. Lecture Notes in Computer Science(), vol 12091. Springer, Cham. https://doi.org/10.1007/978-3-030-48478-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-48478-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48477-4

  • Online ISBN: 978-3-030-48478-1

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