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Metric inference for social networks

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Abstract

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).

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Author notes

  1. David Banks

    Present address: Department of Statistics, Carnegie Mellon University, 15213, Pittsburgh, PA, USA

  2. Kathleen Carley

    Present address: Department of Social and Decision Sciences, Carnegie Mellon University, 15213, Pittsburgh, PA, USA

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    Authors
    1. David Banks
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    2. Kathleen Carley
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    Additional information

    We thank Ove Frank, David Krackhardt, the editor and the referees for their constructive comments and suggestions.

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    Banks, D., Carley, K. Metric inference for social networks. Journal of Classification 11, 121–149 (1994). https://doi.org/10.1007/BF01201026

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