  Social networks are organized into communities with dense internal
connections, giving rise to high values of the clustering coefficient. In
addition, these networks have been observed to be assortative, i.e. highly
connected vertices tend to connect to other highly connected vertices, and have
broad degree distributions. We present a model for an undirected growing
network which reproduces these characteristics, with the aim of producing
efficiently very large networks to be used as platforms for studying
sociodynamic phenomena. The communities arise from a mixture of random
attachment and implicit preferential attachment. The structural properties of
the model are studied analytically and numerically, using the
  $k$-clique method for quantifying the communities.
