Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Authors: Kexin Huang, Ying Jin, Emmanuel Candes, Jure Leskovec
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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] |