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Brief Announcement: Node Sampling Using Centrifugal Random Walks

  • Andrés Sevilla
  • Alberto Mozo
  • Antonio Fernández Anta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7611)

Abstract

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter.

Keywords

Source Node Network Node Connected Network Overlay Network Small World Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrés Sevilla
    • 1
  • Alberto Mozo
    • 2
  • Antonio Fernández Anta
    • 3
  1. 1.Dpto Informática AplicadaU. Politécnica de MadridMadridSpain
  2. 2.Dpto Arquitectura y Tecnología de ComputadoresU. Politécnica de MadridMadridSpain
  3. 3.Institute IMDEA NetworksMadridSpain

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