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1 results

  • 21 - Sampling from Networks

  • from Part IV - Network Inference
  • A. D. Barbour, Universität Zürich, Gesine Reinert, University of Oxford
  • Book:
    Networks
    Published online:
    11 June 2026
    Print publication:
    25 June 2026, pp 609-647

  • Summary

    Many networks are not completely known; the only access to them is by taking samples. This chapter presents methods for deducing information about the whole network from samples. First, some classical sampling methods are briefly considered; random sampling, with and without replacement, stratified sampling and the Horvitz–Thompson estimator. Then sampling methods based on the network structure are introduced, including two-level sampling, induced subgraph sampling, star and snowball sampling and traversal sampling. The differences between the structure of sample networks and those of the parent network are illustrated for some simple models. Finally, the problem of assessing whether a particular network sample is `interesting’ is discussed; interesting, in that it differs from what might be expected of a typical network sample.