  Graph processes that unfold in continuous time are of obvious theoretical and
practical interest. Particularly useful are those whose long-term behavior
converges to a graph distribution of known form. Here, we review some of the
conditions for such convergence, and provide examples of novel and/or known
processes that do so. These include subfamilies of the well-known stochastic
actor oriented models, as well as continuum extensions of temporal and
separable temporal exponential family random graph models. We also comment on
some related threads in the broader work on network dynamics, which provide
additional context for the continuous time case.
