  We study minimax lower bounds for function estimation problems on large graph
when the target function is smoothly varying over the graph. We derive minimax
rates in the context of regression and classification problems on graphs that
satisfy an asymptotic shape assumption and with a smoothness condition on the
target function, both formulated in terms of the graph Laplacian.
