8790 2023 eng 166 article 0 2023-09-20 2023-09-30 -- We consider time-continuous Markovian discrete-state dynamics on random networks of interacting agents and study the large population limit. The dynamics are projected onto low-dimensional collective variables given by the shares of each discrete state in the system, or in certain subsystems, and general conditions for the convergence of the collective variable dynamics to a mean-field ordinary differential equation are proved. We discuss the convergence to this mean-field limit for a continuous-time noisy version of the so-called "voter model" on Erdős-Rényi random graphs, on the stochastic block model, as well as on random regular graphs. Moreover, a heterogeneous population of agents is studied. For each of these types of interaction networks, we specify the convergence conditions in dependency on the corresponding model parameters. Stochastic Processes and their Applications 10.1016/j.spa.2023.09.007 2210.02934 publish yes 2023-09-13 Marvin Lücke Stefanie Winkelmann Jobst Heitzig Péter Koltai Nora Molkethin Stefanie Winkelmann Numerical Mathematics Computational Systems Biology Winkelmann, Stefanie Modeling and Simulation of Complex Processes Lücke, Marvin MathPlusEF4-8