Complex networks describe a wide range of systems in nature and society.
Frequently cited examples
include the cell, a network of chemicals linked by chemical reactions,
and the Internet, a network of
routers and computers connected by physical links. While traditionally
these systems have been
modeled as random graphs, it is increasingly recognized that the topology
and evolution of real
networks are governed by robust organizing principles. This article
reviews the recent advances in the
field of complex networks, focusing on the statistical mechanics of
network topology and dynamics.
After reviewing the empirical data that motivated the recent interest
in networks, the authors discuss
the main models and analytical tools, covering random graphs, small-world
and scale-free networks,
the emerging theory of evolving networks, and the interplay between
topology and the network’s
robustness against failures and attacks.
%0 Journal Article
%1 aAlbertBarabasi2002
%A Albert, R.
%A Barabasi, A.-L.
%D 2002
%J Review of Modern Physics
%K Complex-Networks
%N 1
%P 47-97
%R 10.1103/RevModPhys.74.47
%T Statistical mechanics of complex networks
%V 74
%X Complex networks describe a wide range of systems in nature and society.
Frequently cited examples
include the cell, a network of chemicals linked by chemical reactions,
and the Internet, a network of
routers and computers connected by physical links. While traditionally
these systems have been
modeled as random graphs, it is increasingly recognized that the topology
and evolution of real
networks are governed by robust organizing principles. This article
reviews the recent advances in the
field of complex networks, focusing on the statistical mechanics of
network topology and dynamics.
After reviewing the empirical data that motivated the recent interest
in networks, the authors discuss
the main models and analytical tools, covering random graphs, small-world
and scale-free networks,
the emerging theory of evolving networks, and the interplay between
topology and the network’s
robustness against failures and attacks.
@article{aAlbertBarabasi2002,
abstract = {Complex networks describe a wide range of systems in nature and society.
Frequently cited examples
include the cell, a network of chemicals linked by chemical reactions,
and the Internet, a network of
routers and computers connected by physical links. While traditionally
these systems have been
modeled as random graphs, it is increasingly recognized that the topology
and evolution of real
networks are governed by robust organizing principles. This article
reviews the recent advances in the
field of complex networks, focusing on the statistical mechanics of
network topology and dynamics.
After reviewing the empirical data that motivated the recent interest
in networks, the authors discuss
the main models and analytical tools, covering random graphs, small-world
and scale-free networks,
the emerging theory of evolving networks, and the interplay between
topology and the network’s
robustness against failures and attacks.},
added-at = {2009-09-15T18:15:02.000+0200},
author = {Albert, R. and Barabasi, A.-L.},
biburl = {https://www.bibsonomy.org/bibtex/2d7149a0ac2c1414e40797a70fb5cefe6/tipanverella},
doi = {10.1103/RevModPhys.74.47},
file = {:/home/tiparis/Documents/MenLi/Readings/aAlbertBarabasi2002.pdf:PDF},
interhash = {a35a2b3e25194fdaa4e569fa4447bb9d},
intrahash = {d7149a0ac2c1414e40797a70fb5cefe6},
journal = {Review of Modern Physics},
keywords = {Complex-Networks},
number = 1,
pages = {47-97},
timestamp = {2009-09-16T04:12:14.000+0200},
title = {Statistical mechanics of complex networks},
volume = 74,
year = 2002
}