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Cited by 493

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Properties of highly clustered networks

Open access
Newman, M. E. J.·2003·Physical Review E

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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Crossref · 10.1103/physreve.68.026121

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Metadata

Authors
Newman, M. E. J.
Venue
Physical Review E
Published
2003-08-21
Volume
68
Issue
2
Pages
026121-026121
ISSN
  • 1063-651X (ISSN-L)
  • 1095-3787
OpenAlex
W2030838786
Cited by
493
References
2
FWCI
13.60
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Properties of highly clustered networks. https://app.cochat.ai/papers/10.1103/physreve.68.026121
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Newman, M. E. J. (2003). Properties of highly clustered networks. Physical Review E. https://doi.org/10.1103/physreve.68.026121

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.