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

Training Stable Graph Neural Networks Through Constrained Learning