Journal of Classification

, Volume 15, Issue 2, pp 199–223

Metric Models for Random Graphs

  • David Banks
  • G.M. Constantine

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations