Journal of Classification

, Volume 15, Issue 2, pp 199–223

Metric Models for Random Graphs

  • David Banks
  • G.M. Constantine

DOI: 10.1007/s003579900031

Cite this article as:
Banks, D. & Constantine, G. J. of Classification (1998) 15: 199. doi:10.1007/s003579900031


Many problems entail the analysis of data that are independent and identically distributed random graphs. Useful inference requires flexible probability models for such random graphs; these models should have interpretable location and scale parameters, and support the establishment of confidence regions, maximum likelihood estimates, goodness-of-fit tests, Bayesian inference, and an appropriate analogue of linear model theory. Banks and Carley (1994) develop a simple probability model and sketch some analyses; this paper extends that work so that analysts are able to choose models that reflect application-specific metrics on the set of graphs. The strategy applies to graphs, directed graphs, hypergraphs, and trees, and often extends to objects in countable metric spaces.

Copyright information

© 1998 Springer-Verlag New York Inc.

Authors and Affiliations

  • David Banks
    • 1
  • G.M. Constantine
    • 2
  1. 1.National Institute of Standards and TechnologyUS
  2. 2.University of PittsburghUS