  In this paper we devise a generative random network model with core-periphery
properties whose core nodes act as sublinear dominators, that is, if the
network has $n$ nodes, the core has size $o(n)$ and dominates the entire
network. We show that instances generated by this model exhibit power law
degree distributions, and incorporates small-world phenomena. We also fit our
model in a variety of real-world networks.
