Abstract
This paper presents a statistically sound method for using likelihood to assess potential models of network evolution. The method is tested on data from five real networks. Data from the internet autonomous system network, from two photo sharing sites and from a co-authorship network are tested using this framework.
Summary
Applies a statistically sound likelihood-based method for assessing models of network evolution to five real networks — the internet autonomous-system network, two photo-sharing sites, and a co-authorship network — to see how well proposed evolution models explain real-world data.
bibtex
@inproceedings{clegg2009simplex,
author = {Richard G. Clegg and Raul Landa and Uli Harder and Miguel Rio},
title = {A likelihood based framework for assessing network evolution models tested on real network data},
booktitle = {SIMPLEX '09: 1st ACM Workshop on Simplifying Complex Networks for Practitioners},
year = {2009}
}
Authors
Richard G. Clegg, Raul Landa, Uli Harder, Miguel Rio
Venue
SIMPLEX '09: 1st ACM Workshop on Simplifying Complex Networks for Practitioners, Venice, Italy