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Testing Goodness of Fit of Random Graph Models

Department of Probability Theory and Statistics, Eötvös Loránd University, Budapest, 1053, Hungary
Alfréd Rényi Mathematical Institute of the Hungarian Academy of Sciences, Budapest, 1053, Hungary
Department of Mathematics, Rutgers University, New Brunswick, NJ 08901, USA
Statistics Program, University of Delaware, Newark, DE 19716, USA
Author to whom correspondence should be addressed.
Algorithms 2012, 5(4), 629-635;
Received: 7 May 2012 / Revised: 8 November 2012 / Accepted: 30 November 2012 / Published: 6 December 2012
PDF [136 KB, uploaded 6 December 2012]


Random graphs are matrices with independent 0–1 elements with probabilities determined by a small number of parameters. One of the oldest models is the Rasch model where the odds are ratios of positive numbers scaling the rows and columns. Later Persi Diaconis with his coworkers rediscovered the model for symmetric matrices and called the model beta. Here we give goodness-of-fit tests for the model and extend the model to a version of the block model introduced by Holland, Laskey and Leinhard. View Full-Text
Keywords: random graph; maximum likelihood; rank entropy random graph; maximum likelihood; rank entropy
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Csiszár, V.; Hussami, P.; Komlós, J.; Móri, T.F.; Rejtõ, L.; Tusnády, G. Testing Goodness of Fit of Random Graph Models. Algorithms 2012, 5, 629-635.

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