Node Sampling Using Random Centrifugal Walks

  • Andrés Sevilla
  • Alberto Mozo
  • Antonio Fernández Anta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7702)


Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the source, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it.

Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes’ weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter.

Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source.

The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution.


Span Tree Connected Network Overlay Network Attachment Point Node Sampling 
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.


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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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