  Network representations can help reveal the behavior of complex systems.
Useful information can be derived from the network properties and invariants,
such as components, clusters or cliques, as well as from their changes over
time. The evolution of clusters of nodes (or communities) is one of the major
focus of research. However, the time dimension increases complexity,
introducing new constructs and requiring novel and enhanced algorithms. In
spite of recent improvements, the relative scarcity of timestamped
representations of empiric networks, with known ground truth, hinders algorithm
validation. A few approaches have been proposed to generate synthetic temporal
networks that conform to static topological specifications while in general
adopting an ad-hoc approach to temporal evolution. We believe there is still a
need for a principled synthetic network generator that conforms to problem
domain topological specifications from a static as well as temporal
perspective. Here we present such a system. The unique attributes of our system
include accepting arbitrary node degree and cluster size distributions and
temporal evolution under user control, while supporting tunable joint
distribution and temporal correlation of node degrees. Theoretical
contributions include the analysis of conditions for "graphability" of
sequences of inter and intra cluster node degrees and cluster sizes and the
development of a heuristic to search for the cluster membership of nodes that
minimizes the shared information distance between clusterings. Our work shows
that this system is capable of generating networks under user controlled
topology with up to thousands of nodes and hundreds of clusters with strong
topology adherence. Much larger networks are possible with relaxed
requirements. The generated networks support algorithm validation as well as
problem domain analysis.
