Next Article in Journal
Edge Detection from MRI and DTI Images with an Anisotropic Vector Field Flow Using a Divergence Map
Previous Article in Journal
Laplace–Fourier Transform of the Stretched Exponential Function: Analytic Error Bounds, Double Exponential Transform, and Open-Source Implementation “libkww”
Algorithms 2012, 5(4), 629-635; doi:10.3390/a5040629

Testing Goodness of Fit of Random Graph Models

2,4,*  and 2
1 Department of Probability Theory and Statistics, Eötvös Loránd University, Budapest, 1053, Hungary 2 Alfréd Rényi Mathematical Institute of the Hungarian Academy of Sciences, Budapest, 1053, Hungary 3 Department of Mathematics, Rutgers University, New Brunswick, NJ 08901, USA 4 Statistics Program, University of Delaware, Newark, DE 19716, USA
* Author to whom correspondence should be addressed.
Received: 7 May 2012 / Revised: 8 November 2012 / Accepted: 30 November 2012 / Published: 6 December 2012
View Full-Text   |   Download PDF [136 KB, 6 December 2012; original version 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.
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).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
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.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert