The big friendly giant: the giant component in clustered random graphs

Yakir Berchenko*, Yael Artzy-Randrup, Mina Teicher, Lewi Stone

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Network theory is a powerful tool for describing and modeling complex systems having applications in widelydiffering areas including epidemiology [16], neuroscience [34], ecology [20] and the Internet [26]. In its beginning, one often compared an empirically given network, whose nodes are the elements of the system and whose edges represent their interactions, with an ensemble having the same number of nodes and edges, the most popular example being the random graphs introduced by Erdos and Renyi [11]. As the field matured, it became clear that the naive model above needed to be refined, due to the observation that real-world networks often differ significantly from the Erdos–Renyi random graphs in having a highly heterogenous non-Poisson degree distribution [5, 15] and in possessing a high level of clustering [33]. Methods for generating random networks with arbitrary degree distributions and for calculating their statistical properties are now well understood.

Original languageEnglish
Title of host publicationModeling and Simulation in Science, Engineering and Technology
PublisherSpringer Basel
Pages237-252
Number of pages16
DOIs
StatePublished - 2009

Publication series

NameModeling and Simulation in Science, Engineering and Technology
Volume42
ISSN (Print)2164-3679
ISSN (Electronic)2164-3725

Funding

FundersFunder number
National Science Foundation
James S. McDonnell Foundation

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