TY  - GEN
AU  - Newman, M. E. J.
A2  - 
T1  - The structure and function of complex networks
JO  - 
PB  - 
C1  - 
PY  - 2003/03
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/cond-mat/0303516
DO  - 
KW  - folksonomy_socialnetwork
KW  - information
KW  - small_world
KW  - diploma_thesis
KW  - complex_systems
KW  - eventually_useful
KW  - web_graph
KW  - scale_free_networks
KW  - clustering
L1  - newman03-structure.pdf
N1  - 
N1  - 
AB  - 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.
ER  -

TY  - GEN
AU  - Newman, M. E. J.
A2  - 
T1  - The structure and function of complex networks
JO  - 
PB  - 
C1  - 
PY  - 2003/03
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/cond-mat/0303516
DO  - 
KW  - algorithm
KW  - clustering
KW  - complex_systems
KW  - folksonomy
KW  - information
KW  - kdubiq
KW  - network
KW  - retrieval
KW  - scale_free_networks
KW  - small
KW  - socialnetwork
KW  - summerschool
KW  - theory
KW  - web
KW  - web_graph
KW  - world
L1  - 
N1  - 
N1  - 
AB  - Inspired by empirical studies of networked systems such as the Internet,
<p>social networks, and biological networks, researchers have in recent years
<p>developed a variety of techniques and models to help us understand or predict
<p>the behavior of these systems. Here we review developments in this field,
<p>including such concepts as the small-world effect, degree distributions,
<p>clustering, network correlations, random graph models, models of network growth
<p>and preferential attachment, and dynamical processes taking place on networks.
ER  -

TY  - GEN
AU  - Flake, Gary
AU  - Lawrence, Steve
AU  - Giles, Lee L.
A2  - 
T1  - Efficient Identification of Web Communities
JO  - 
PB  - 
C1  - Boston, MA
PY  - 2000/august february0--februarymarch
VL  - 
IS  - 
SP  - 150
EP  - 160
UR  - http://citeseer.ist.psu.edu/flake00efficient.html
DO  - 
KW  - clustering
KW  - max_flow
KW  - web
KW  - web_graph
KW  - community
L1  - 
N1  - 
N1  - 
AB  - We define a community on the web as a set of sites that have more links (in either direction) to members of the community than to non-members. Members of such a community can be efficiently identified in a maximum flow / minimum cut framework, where the source is composed of known members, and the sink consists of well-known non-members. A focused crawler that crawls to a fixed depth can approximate community membership by augmenting the graph induced by the crawl with links to a virtual sink...
ER  -