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NeurIPS 2026CNRSUiT the Arctic University of NorU TromsøGraph neural networks

A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks

<|message_model|><|content_text|>The Tikhonov layer is an interpretable graph neural network layer whose learnable parameters directly reveal which node features and topological aspects drive predictions. Its closed-form propagation solves a generalized graph Tikhonov problem, yielding built-in expl

Nicolas Tremblay, Filippo Maria Bianchi, Benjamin Ricaud

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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medium 8/10
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NeurIPS 2026U Roma &quot;Tor Vergata&quot;U BaselU Rome Tor VergataBocconiUiT the Arctic University of NorGraph neural networks

Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry–Task Alignment

Hyperbolic graph neural networks outperform Euclidean models only when tasks align with hyperbolic geometry, not merely when graphs are tree-like.

Dionisia Naddeo, Jonas Linkerhägner, Nicola Toschi, Geri Skenderi and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5