  Given a graph with a subset of labeled nodes, we are interested in the
quality of the averaging estimator which for an unlabeled node predicts the
average of the observations of its labeled neighbors. We rigorously study
concentration properties, variance bounds and risk bounds in this context.
While the estimator itself is very simple we believe that our results will
contribute towards the theoretical understanding of learning on graphs through
more sophisticated methods such as Graph Neural Networks.
