Can Adaptive Diffusion Networks Do Better with Less Data?

In this paper, we analyze the performance of an algorithm for adaptive diffusion networks that controls the number of nodes sampled per iteration based on the estimation error. The goal of this solution is to keep the nodes sampled while the estimation error is high in magnitude, and to cease their sampling when it is sufficiently low. Our model shows that this approach can preserve the convergence rate in comparison with the case in which every node is sampled permanently, while slightly improving the steady-state performance.