Sampling and Estimation in Network Graphs

Part of the Springer Series in Statistics book series (SSS)

Frequently it is the case that relational information is observed on only a portion of a complex system being studied, and the network resulting from such measurements may be thought of as a sample from a larger underlying network. If the goal is to use the sampled network data to infer properties of the underling network, this task may be approached using principles of statistical sampling theory. However, sampling in a network context introduces various potential complications. In this chapter we formalize the problem of sampling and estimation in network graphs, describe a handful of common network sampling designs, and develop estimators of a number of quantities of interest.


Degree Distribution Betweenness Centrality Simple Random Sampling Network Graph Inclusion Probability 


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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  1. 1.Dept. Mathematics & StatisticsBoston UniversityBostonUSA

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