We argue that social networks differ from most other types of networks,
including technological and biological networks, in two important ways. First,
they have non-trivial clustering or network transitivity, and second, they show
positive correlations, also called assortative mixing, between the degrees of
adjacent vertices. Social networks are often divided into groups or
communities, and it has recently been suggested that this division could
account for the observed clustering. We demonstrate that group structure in
networks can also account for degree correlations. We show using a simple model
that we should expect assortative mixing in such networks whenever there is
variation in the sizes of the groups and that the predicted level of
assortative mixing compares well with that observed in real-world networks.
%0 Generic
%1 citeulike:336118
%A Newman, M. E. J.
%A Park, Juyong
%D 2003
%K networks social
%T Why social networks are different from other types of networks
%U http://arxiv.org/abs/cond-mat/0305612
%X We argue that social networks differ from most other types of networks,
including technological and biological networks, in two important ways. First,
they have non-trivial clustering or network transitivity, and second, they show
positive correlations, also called assortative mixing, between the degrees of
adjacent vertices. Social networks are often divided into groups or
communities, and it has recently been suggested that this division could
account for the observed clustering. We demonstrate that group structure in
networks can also account for degree correlations. We show using a simple model
that we should expect assortative mixing in such networks whenever there is
variation in the sizes of the groups and that the predicted level of
assortative mixing compares well with that observed in real-world networks.
@misc{citeulike:336118,
abstract = {We argue that social networks differ from most other types of networks,
including technological and biological networks, in two important ways. First,
they have non-trivial clustering or network transitivity, and second, they show
positive correlations, also called assortative mixing, between the degrees of
adjacent vertices. Social networks are often divided into groups or
communities, and it has recently been suggested that this division could
account for the observed clustering. We demonstrate that group structure in
networks can also account for degree correlations. We show using a simple model
that we should expect assortative mixing in such networks whenever there is
variation in the sizes of the groups and that the predicted level of
assortative mixing compares well with that observed in real-world networks.},
added-at = {2007-12-05T08:02:28.000+0100},
author = {Newman, M. E. J. and Park, Juyong},
biburl = {https://www.bibsonomy.org/bibtex/2c91d3a94b19cdbfa9b1c93c0ae8e051b/jhammerb},
citeulike-article-id = {336118},
comment = {Another Newman paper. This one from 2003 on how social networks are unique.},
eprint = {cond-mat/0305612},
interhash = {c074e9640dd0a12bdcb5165afcab5981},
intrahash = {c91d3a94b19cdbfa9b1c93c0ae8e051b},
keywords = {networks social},
month = May,
priority = {2},
timestamp = {2007-12-05T08:27:44.000+0100},
title = {Why social networks are different from other types of networks},
url = {http://arxiv.org/abs/cond-mat/0305612},
year = 2003
}