  Graph Neural Networks (GNN) rely on graph convolutions to learn features from
network data. GNNs are stable to different types of perturbations of the
underlying graph, a property that they inherit from graph filters. In this
paper we leverage the stability property of GNNs as a typing point in order to
seek for representations that are stable within a distribution. We propose a
novel constrained learning approach by imposing a constraint on the stability
condition of the GNN within a perturbation of choice. We showcase our framework
in real world data, corroborating that we are able to obtain more stable
representations while not compromising the overall accuracy of the predictor.

Machine Learning;Signal Processing