Research map / Graph learning / GNN architectures
Distribution Free Prediction Sets for Node Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Localize APS calibration to the test node’s 2-hop neighbourhood | GraphSAGE-Mean with APS · Reddit2 | average prediction set size: 2.02 -> 1.59 | Helped |
| Localize APS calibration to the test node’s 2-hop neighbourhood | GraphSAGE-Mean with APS · Flickr | average prediction set size: 4.22 -> 4.01 | Helped |
| Localize APS calibration to the test node’s 2-hop neighbourhood | GraphSAGE-Mean with APS · Amazon Computers | average prediction set size: 1.50 -> 1.44 | Helped |
| Localize APS calibration to the test node’s 2-hop neighbourhood | APS with the evaluated GNN models · Reddit2, Flickr, Amazon Computers | SSCV | Helped |
| Localize APS calibration to the test node’s 2-hop neighbourhood | APS with the evaluated GNN models · Reddit2, Flickr, Amazon Computers | PCCV | Helped |
| Use hyperbolic distance weights instead of uniform neighbourhood weights (ablation) | NAPS with GraphSAGE-Mean · Flickr | average prediction set size: 4.01 -> 4.10 | Hurt |
| Use geometric distance weights instead of uniform neighbourhood weights (ablation) | NAPS with GraphSAGE-Mean · Flickr | average prediction set size: 4.01 -> 4.12 | Hurt |
| Increase neighbourhood depth K (ablation) | Unweighted NAPS with GraphSAGE-Mean · Reddit2 | average prediction set size | Mixed |
| Use distance-decayed calibration weights as neighbourhood depth grows (ablation) | NAPS-H and NAPS-G with GraphSAGE-Mean · Reddit2 | average prediction set size | Helped |
Closest papers by meaning
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- GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs (NeurIPS 2026 · similarity 0.89)
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