NDLI: Metric Models for Random Graphs

Content Provider	Springer Nature Link
Author	Banks, David Constantine, G.M.
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.
Starting Page	199
Ending Page	223
Page Count	25
File Format	PDF
ISSN	01764268
Journal	Journal of Classification
Volume Number	15
Issue Number	2
e-ISSN	14321343
Language	English
Publisher	Springer-Verlag
Publisher Date	1998-02-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Mathematics Psychology Library and Information Sciences Statistics, Probability and Uncertainty

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Metric Models for Random Graphs