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Estimating the Parameters of the Waxman Random Graph

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Algorithms and Models for the Web Graph (WAW 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11631))

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Abstract

The Waxman random graph is useful for modelling physical networks where the increased cost of longer links means they are less likely to be built, and thus less numerous. The model has been in continuous use for over three decades with many attempts to match parameters to real networks, but only a few cases where a formal estimator was used. Even then the performance of the estimator was not evaluated. This paper presents both the first evaluation of formal estimators for these graphs, and a new Maximum Likelihood Estimator with O(e) computational complexity where e is the number of edges in the graph, and requiring only link lengths as input, as compared to all other algorithms which are \(\varOmega (n^2)\).

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Acknowledgements

We would like to thank Lakhina et al. for providing the Internet dataset.

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Authors and Affiliations

  1. ARC Centre of Excellence for Mathematical and Statistical Frontiers, University of Adelaide, Adelaide, Australia

    Matthew Roughan, Jonathan Tuke & Eric Parsonage

Authors
  1. Matthew Roughan
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  2. Jonathan Tuke
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  3. Eric Parsonage
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Corresponding author

Correspondence to Matthew Roughan .

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Editors and Affiliations

  1. Inria, Sophia Antipolis, France

    Konstantin Avrachenkov

  2. Ryerson University, Toronto, ON, Canada

    Paweł Prałat

  3. The University of Queensland, Brisbane, QLD, Australia

    Nan Ye

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Roughan, M., Tuke, J., Parsonage, E. (2019). Estimating the Parameters of the Waxman Random Graph. In: Avrachenkov, K., Prałat, P., Ye, N. (eds) Algorithms and Models for the Web Graph. WAW 2019. Lecture Notes in Computer Science(), vol 11631. Springer, Cham. https://doi.org/10.1007/978-3-030-25070-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-25070-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25069-0

  • Online ISBN: 978-3-030-25070-6

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