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Open AccessJournal ArticleDOI

Properties of highly clustered networks

Mark Newman
- 21 Aug 2003 - 
- Vol. 68, Iss: 2, pp 026121-026121
TLDR
The results indicate that increased clustering leads to a decrease in the size of the giant component of the network, and clustering causes epidemics to saturate sooner, meaning that they infect a near-maximal fraction of thenetwork for quite low transmission rates.
Abstract
We propose and solve exactly a model of a network that has both a tunable degree distribution and a tunable clustering coefficient. Among other things, our results indicate that increased clustering leads to a decrease in the size of the giant component of the network. We also study susceptible/infective/recovered type epidemic processes within the model and find that clustering decreases the size of epidemics, but also decreases the epidemic threshold, making it easier for diseases to spread. In addition, clustering causes epidemics to saturate sooner, meaning that they infect a near-maximal fraction of the network for quite low transmission rates.

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Citations
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Epidemic processes in complex networks

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Modelling disease outbreaks in realistic urban social networks.

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The Web of Human Sexual Contacts

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References
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Journal ArticleDOI

The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI

Community structure in social and biological networks

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