We present an empirical study of different social networks obtained from digital repositories. Our analysis reveals the community structure and provides a useful visualising technique. We investigate the scaling properties of the community size distribution, and that find all the networks exhibit power law scaling in the community size distributions with exponent either -0.5 or -1. Finally we find that the networks' community structure is topologically self-similar using the Horton-Strahler index.
@article{arxiv.cond-mat/0312040,
title = {Community analysis in social networks},
author = {Alex Arenas and Leon Danon and Albert Diaz-Guilera and Pablo M. Gleiser and Roger Guimera},
journal= {arXiv preprint arXiv:cond-mat/0312040},
year = {2009}
}