  The changing topology of a network is driven by the need to maintain or
optimize network function. As this function is often related to moving
quantities such as traffic, information, etc. efficiently through the network
the structure of the network and the dynamics on the network directly depend on
the other. To model this interplay of network structure and dynamics we use the
dynamics on the network, or the dynamical processes the network models, to
influence the dynamics of the network structure, i.e., to determine where and
when to modify the network structure. We model the dynamics on the network
using Jackson network dynamics and the dynamics of the network structure using
minimal specialization, a variant of the more general network growth model
known as specialization. The resulting model, which we refer to as the
integrated specialization model, coevolves both the structure and the dynamics
of the network. We show this model produces networks with real-world
properties, such as right-skewed degree distributions, sparsity, the
small-world property, and non-trivial equitable partitions. Additionally, when
compared to other growth models, the integrated specialization model creates
networks with small diameter, minimizing distances across the network. Along
with producing these structural features, this model also sequentially removes
the network's largest bottlenecks. The result are networks that have both
dynamic and structural features that allow quantities to more efficiently move
through the network.
