  We consider models for network indexed multivariate data involving a
dependence between variables as well as across graph nodes.
  In the framework of these models, we focus on outliers detection and
introduce the concept of edgewise outliers. For this purpose, we first derive
the distribution of some sums of squares, in particular squared Mahalanobis
distances that can be used to fix detection rules and thresholds for outlier
detection. We then propose a robust version of the deterministic MCD algorithm
that we call edgewise MCD. An application on simulated data shows the interest
of taking the dependence structure into account. We also illustrate the utility
of the proposed method with a real data set.
