Newman, M. E. J.
(2003):
*The structure and function of complex networks*.

[Volltext] [Kurzfassung] [BibTeX] [Endnote]

[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Inspired by empirical studies of networked systems such as the Internet,

cial networks, and biological networks, researchers have in recent years

veloped a variety of techniques and models to help us understand or predict

e behavior of these systems. Here we review developments in this field,

cluding such concepts as the small-world effect, degree distributions,

ustering, network correlations, random graph models, models of network growth

d preferential attachment, and dynamical processes taking place on networks.

cial networks, and biological networks, researchers have in recent years

veloped a variety of techniques and models to help us understand or predict

e behavior of these systems. Here we review developments in this field,

cluding such concepts as the small-world effect, degree distributions,

ustering, network correlations, random graph models, models of network growth

d preferential attachment, and dynamical processes taking place on networks.

@misc{citeulike:155,
author = {Newman, M. E. J.},
title = {The structure and function of complex networks},
year = {2003},
url = {http://arxiv.org/abs/cond-mat/0303516},
keywords = {algorithm, clustering, complex_systems, folksonomy, information, kdubiq, network, retrieval, scale_free_networks, small, socialnetwork, summerschool, theory, web, web_graph, world},
abstract = {Inspired by empirical studies of networked systems such as the Internet,

cial networks, and biological networks, researchers have in recent years

veloped a variety of techniques and models to help us understand or predict

e behavior of these systems. Here we review developments in this field,

cluding such concepts as the small-world effect, degree distributions,

ustering, network correlations, random graph models, models of network growth

d preferential attachment, and dynamical processes taking place on networks.} }

cial networks, and biological networks, researchers have in recent years

veloped a variety of techniques and models to help us understand or predict

e behavior of these systems. Here we review developments in this field,

cluding such concepts as the small-world effect, degree distributions,

ustering, network correlations, random graph models, models of network growth

d preferential attachment, and dynamical processes taking place on networks.} }

%0 = misc
%A = Newman, M. E. J.
%D = 2003
%T = The structure and function of complex networks
%U = http://arxiv.org/abs/cond-mat/0303516