Journal of Classification

, Volume 11, Issue 1, pp 121-149

First online:

Metric inference for social networks

  • David BanksAffiliated withDepartment of Statistics, Carnegie Mellon University
  • , Kathleen CarleyAffiliated withDepartment of Social and Decision Sciences, Carnegie Mellon University

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Using a natural metric on the space of networks, we define a probability measure for network-valued random variables. This measure is indexed by two parameters, which are interpretable as a location parameter and a dispersion parameter. From this structure, one can develop maximum likelihood estimates, hypothesis tests and confidence regions, all in the context of independent and identically distributed networks. The value of this perspective is illustrated through application to portions of the friedship cognitive social structure data gathered by Krackhardt (1987).


Random networks Random graphs Digraphs