Testing goodness of fit of random graph models

Villo Csiszár, Péter Hussami, János Komlós, Tamás F. Móri, Lídia Rejto, Gábor Tusnády

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)629-635
Number of pages7
JournalAlgorithms
Volume5
Issue number4
DOIs
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics

Keywords

  • Maximum Likelihood
  • Random Graph
  • Rank Entropy

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    Csiszár, V., Hussami, P., Komlós, J., Móri, T. F., Rejto, L., & Tusnády, G. (2012). Testing goodness of fit of random graph models. Algorithms, 5(4), 629-635. https://doi.org/10.3390/a5040629