  Irregularly sampling a spatially stationary random field does not yield a
graph stationary signal in general. Based on this observation, we build a
definition of graph stationarity based on intrinsic stationarity, a less
restrictive definition of classical stationarity. We introduce the concept of
graph variogram, a novel tool for measuring spatial intrinsic stationarity at
local and global scales for irregularly sampled signals by selecting subgraphs
of local neighborhoods. Graph variograms are extensions of variograms used for
signals defined on continuous Euclidean space. Our experiments with
intrinsically stationary signals sampled on a graph, demonstrate that graph
variograms yield estimates with small bias of true theoretical models, while
being robust to sampling variation of the space.
