  The past analyses of datasets of social networks have enabled us to make
empirical findings of a number of aspects of human society, which are commonly
featured as stylized facts of social networks, such as broad distributions of
network quantities, existence of communities, assortative mixing, and
intensity-topology correlations. Since the understanding of the structure of
these complex social networks is far from complete, for deeper insight into
human society more comprehensive datasets and modeling of the stylized facts
are needed. Although the existing dynamical and static models can generate some
stylized facts, here we take an alternative approach by devising a
community-based static model with heterogeneous community sizes and larger
communities having smaller link density and weight. With these few assumptions
we are able to generate realistic social networks that show most stylized facts
for a wide range of parameters, as demonstrated numerically and analytically.
Since our community-based static model is simple to implement and easily
scalable, it can be used as a reference system, benchmark, or testbed for
further applications.
