pith:UDQL4E2S
Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment
Hyperbolic graph neural networks outperform Euclidean ones only when the task itself requires preserving the graph's metric structure.
arxiv:2602.01828 v2 · 2026-02-02 · cs.LG
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Claims
HGNNs gain an advantage on link prediction, a naturally geometry-aligned task, whereas this advantage largely disappears on standard node classification benchmarks, which are typically not geometry-aligned.
That link prediction is inherently geometry-aligned while standard node classification benchmarks are not, and that the chosen distortion measures and benchmarks generalize beyond the tested cases.
HGNNs recover low-distortion representations and outperform Euclidean models only on geometry-aligned tasks such as link prediction, while the advantage disappears on non-aligned tasks like node classification.
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| First computed | 2026-05-17T23:39:00.097600Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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