Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Authors: Kexin Huang, Ying Jin, Emmanuel Candes, Jure Leskovec
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that CF-GNN achieves any pre-defined target marginal coverage while significantly reducing the prediction set/interval size by up to 74% over the baselines. |
| Researcher Affiliation | Academia | Kexin Huang1 Ying Jin2 Emmanuel Candès2,3 Jure Leskovec1 1 Department of Computer Science, Stanford University 2 Department of Statistics, Stanford University 3 Department of Mathematics, Stanford University |
| Pseudocode | Yes | Algorithm 1: Pseudo-code for CF-GNN algorithm. |
| Open Source Code | Yes | The code is available at https://github.com/snap-stanford/conformalized-gnn. |
| Open Datasets | Yes | For node classification, we use the common node classification datasets in Pytorch Geometric package. For node regression, we use datasets in [20]. |
| Dataset Splits | Yes | For node classification, we follow a standard semi-supervised learning evaluation procedure [24], where we randomly split data into folds with 20%/10%/70% nodes as Dtrain/Dvalid/Dcalib Dtest. |
| Hardware Specification | Yes | Each experiment is done with a single NVIDIA 2080 Ti RTX 11GB GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch Geometric package' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | Table 5: Hyperparameter range for CF-GNN. Task Param. Range Classification GNNϑ Hidden dimension [16,32,64,128,256] Learning rate [1e-1, 1e-2, 1e-3, 1e-4] GNNϑ Number of GNN Layers [1,2,3,4] |