Training Stable Graph Neural Networks Through Constrained Learning

• URL: http://arxiv.org/abs/2110.03576v1
• Date: Thu, 7 Oct 2021 15:54:42 GMT
• Title: Training Stable Graph Neural Networks Through Constrained Learning
• Authors: Juan Cervino, Luana Ruiz and Alejandro Ribeiro
• Abstract summary: Graph Neural Networks (GNNs) 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. We propose a novel constrained learning approach by imposing a constraint on the stability condition of the GNN within a perturbation of choice.
• Score: 116.03137405192356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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