Journal article

+ 2 other files

Statistical validation of high-dimensional models of growing networks

  • Medo, Matúš Physics Department, University of Fribourg, Switzerland
Published in:
  • Physical Review E. - 2014, vol. 89, no. 3, p. 032801
English The abundance of models of complex networks and the current insufficient validation standards make it difficult to judge which models are strongly supported by data and which are not. We focus here on likelihood maximization methods for models of growing networks with many parameters and compare their performance on artificial and real datasets. While high dimensionality of the parameter space harms the performance of direct likelihood maximization on artificial data, this can be improved by introducing a suitable penalization term. Likelihood maximization on real data shows that the presented approach is able to discriminate among available network models. To make large-scale datasets accessible to this kind of analysis, we propose a subset sampling technique and show that it yields substantial model evidence in a fraction of time necessary for the analysis of the complete data.
Faculté des sciences et de médecine
Département de Physique
  • English
License undefined
Persistent URL
Other files


Document views: 50 File downloads:
  • med_svh.pdf: 55
  • EF-links-anonymized.dat: 18
  • EF-metadata-anonymized.dat: 17