CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks

• URL: http://arxiv.org/abs/2409.04159v1
• Date: Fri, 6 Sep 2024 09:43:09 GMT
• Title: CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
• Authors: Clemens Damke, Eyke Hüllermeier,
• Abstract summary: We study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. We propose a family of Committe-based Uncertainty Quantification Graph Neural Networks (CUQ-GNNs)
• Score: 21.602569813024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. Previously, the so-called Graph Posterior Network (GPN) model has been proposed to quantify uncertainty in node classification tasks. Given a graph, it uses Normalizing Flows (NFs) to estimate class densities for each node independently and converts those densities into Dirichlet pseudo-counts, which are then dispersed through the graph using the personalized Page-Rank algorithm. The architecture of GPNs is motivated by a set of three axioms on the properties of its uncertainty estimates. We show that those axioms are not always satisfied in practice and therefore propose the family of Committe-based Uncertainty Quantification Graph Neural Networks (CUQ-GNNs), which combine standard Graph Neural Networks with the NF-based uncertainty estimation of Posterior Networks (PostNets). This approach adapts more flexibly to domain-specific demands on the properties of uncertainty estimates. We compare CUQ-GNN against GPN and other uncertainty quantification approaches on common node classification benchmarks and show that it is effective at producing useful uncertainty estimates.

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