Metric Models for Random Graphs |
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Authors: | David Banks GM Constantine |
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Institution: | (1) National Institute of Standards and Technology, US;(2) University of Pittsburgh, US |
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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. |
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Keywords: | |
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