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.

@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, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, 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.} }

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