CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
<|reference_start|>CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks: 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.<|reference_end|>
@article{damke2024cuq-gnn:,
    title={CUQ-GNN: Committee-based Graph Uncertainty Quantification using
  Posterior Networks},
    author={Clemens Damke, Eyke H"ullermeier},
    journal={arXiv preprint arXiv:2409.04159},
    year={2024},
    doi={10.1007/978-3-031-70371-3_18},
    archivePrefix={arXiv},
    eprint={2409.04159},
    primaryClass={cs.LG stat.ML}
}