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Metric Models for Random Graphs

Published

Author(s)

D L. Banks, G 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 establishement 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.
Citation
Journal of Classification
Volume
15

Keywords

Bernoulli graphs, clustering, Gibbs distribution, Holland-Leinhardt models, phylogney, trees

Citation

Banks, D. and Constantine, G. (1998), Metric Models for Random Graphs, Journal of Classification, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151732 (Accessed June 19, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 30, 1998, Updated February 17, 2017