Journal of Classification

, Volume 11, Issue 1, pp 121–149

Metric inference for social networks

  • David Banks
  • Kathleen Carley

DOI: 10.1007/BF01201026

Cite this article as:
Banks, D. & Carley, K. Journal of Classification (1994) 11: 121. doi:10.1007/BF01201026


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 networksRandom graphsDigraphs

Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • David Banks
    • 1
  • Kathleen Carley
    • 2
  1. 1.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Social and Decision SciencesCarnegie Mellon UniversityPittsburghUSA