Clustering in complex networks. II. Percolation properties
Disordered Systems and Neural Networks
2009-11-11 v2
Abstract
The percolation properties of clustered networks are analyzed in detail. In the case of weak clustering, we present an analytical approach that allows to find the critical threshold and the size of the giant component. Numerical simulations confirm the accuracy of our results. In more general terms, we show that weak clustering hinders the onset of the giant component whereas strong clustering favors its appearance. This is a direct consequence of the differences in the -core structure of the networks, which are found to be totally different depending on the level of clustering. An empirical analysis of a real social network confirms our predictions.
Cite
@article{arxiv.cond-mat/0608337,
title = {Clustering in complex networks. II. Percolation properties},
author = {M. Angeles Serrano and Marian Boguna},
journal= {arXiv preprint arXiv:cond-mat/0608337},
year = {2009}
}
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