The main result presented in this paper (whose proof can be found in [1]) is that the fraction of agents (Y̅^N_k(t)) at state k ∈ X := {1, ..., K} associated with an interacting particle system over an appropriate dynamical communication network converges weakly to the solution of a differential equation. The vector macroprocess (Y̅^N(t)) = (Y̅^N₁(t),..., Y̅^N_k(t)) is not Markov since its evolution depends not only on its current state, but on finer real-time microscopic high-dimensional information of the system - namely, the state of the N nodes X^N(t) ∈ X^N. Our result essentially states that under an appropriate dynamics of the underlying network of contacts, the macroprocess (Y̅^N(t)) becomes asymptotically (in N) Markov.