<p>The goal of this work is to identify steady-state solutions to dynamical systems defined on large, random families of networks. We do so by passing to a continuum limit where the adjacency matrix is replaced by a nonlocal operator with kernel called a graphon. This graphon equation is often more amenable to analysis and provides a single equation to study instead of the infinitely many variations of networks that lead to the limit. Our work establishes a rigorous connection between steady states of the continuum and network systems. Precisely, we show that if the graphon equation has a steady-state solution and if the linearization of the equation at that solution is invertible, then there exist related steady-state solutions to the finite-dimensional networked dynamical system over all sufficiently large graphs converging to the graphon. The proof involves setting up a Newton–Kantorovich type iteration scheme which is shown to be a contraction on a suitable metric space. Interestingly, we show that the first iterate of our defined operator in general fails to be a contraction mapping, but the second iterate is proven to contract on the space. We extend our results to show that linear stability properties further carry over from the graphon system to the networked dynamical system. Our results are applied to twisted states in a Kuramoto model of coupled oscillators, steady states in a model of neuronal network activity, and a Lotka–Volterra model of ecological interaction.</p>