Thermodynamic Limit of Interacting Particle Systems over Time-varying Sparse Random Networks

We establish a functional weak law of large numbers for observable macroscopic state variables of interacting particle systems (e.g., voter and contact processes) over fast time-varying sparse random networks of interactions. We show that, as the number of agents $N$ grows large, the proportion of agents $left(overline{Y}_{k}^{N}(t)right)$ at a certain state $k$ converges in distribution -- or, more precisely, weakly with respect to the uniform topology on the space of emph{c`adl`ag} sample paths -- to the solution of an ordinary differential equation over any compact interval $left[0,Tright]$. Although the limiting process is Markov, the prelimit processes, i.e., the normalized macrostate vector processes $left(mathbf{overline{Y}}^{N}(t)right)=left(overline{Y}_{1}^{N}(t),ldots,overline{Y}_{K}^{N}(t)right)$, are non-Markov as they are tied to the emph{high-dimensional} microscopic state of the system, which precludes the direct application of standard arguments for establishing weak convergence. The techniques developed in the paper for establishing weak convergence might be of independent interest.