Long-Range Graph Learning

Latest papers

11 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Oscillatory Neural Dynamics over Sheaves
Oct 7, 2026Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina +5Long-Range Graph Learning Graph Neural Networks
FaBiT, University of Bologna · Department of Computer Science, University of Pisa · Department of Computer, Control and Management Engineering, Sapienza University of Rome +1
Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing
Oct 2, 2026Ferran Hernandez Caralt, Simon Heilig, Adrián Bazaga +3Long-Range Graph Learning Over-Squashing in GNNs
Department of Computer Science and Technology, University of Cambridge, UK · Faculty of Computer Science, Ruhr University Bochum, Germany · Microsoft, London, UK +1
Reachability is not enough: Diagnosing long-range behavior in GNNs
Sep 27, 2026Filippo Maria BianchiGraph Neural Networks GNN Expressivity
UiT The Arctic University of Norway · NORCE Norwegian Research Centre
HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models
Aug 10, 2026Isuru Herath, Arin Gopakumar, Sharan SahuLong-Range Graph Learning Graph Representation Learning
Carnegie Mellon University · UC Berkeley · Cornell University
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
Jun 3, 2026Ayushman Raghuvanshi, Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri +1Long-Context Modeling Temporal Link Prediction
Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore
S $^3$ GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
May 22, 2026Dai Shi, Luke Thompson, Linhan Luo +4Long-Range Graph Learning Graph Neural Networks
Department of Engineering, University of Cambridge, Cambridge, UK · University of Sydney, Australia
Graph Hierarchical Recurrence for Long-Range Generalization
May 18, 2026Stefano Carotti, Marco Pacini, Alessio Gravina +3Long-Range Graph Learning Graph Neural Networks
Department of Computer Science, University of Trento, Italy. · Fondazione Bruno Kessler, Italy. · Department of Computer Science, University of Pisa, Italy.
From Message-Passing to Linearized Graph Sequence Models
May 12, 2026Joël Mathys, Basil Rohner, Saku Peltonen +1Long-Range Graph Learning Graph Representation Learning
1ETH Zurich, Switzerland
Can Graph Foundation Models Generalize Over Architecture?
Mar 24, 2026Benjamin Gutteridge, Michael Bronstein, Xiaowen DongLong-Range Graph Learning Graph Foundation Models
University of Oxford · AITHYRA
L2G-Net: Local to Global Spectral Graph Neural Networks via Cauchy Factorizations
Feb 21, 2026Samuel Fernández-Menduiña, Eduardo Pavez, Antonio OrtegaSpectral Graph Convolution Graph Signal Processing
University of Southern California.
Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs
Feb 20, 2026Mia Zosso, Ali Hariri, Victor Kawasaki-Borruat +2Long-Range Graph Learning Graph Representation Learning
École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · École Normale Supérieure – PSL, Paris, France

Oct 7, 2026J/K move · Enter open · S save

Oscillatory Neural Dynamics over Sheaves

Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina, Alessio Borgi+4

FaBiT, University of Bologna · Department of Computer Science, University of Pisa · Department of Computer, Control and Management Engineering, Sapienza University of Rome+1

Long-Range Graph Learning Graph Neural Networks Graph Representation Learning Sheaf Neural Networks Oscillatory Neural Networks GNN Expressivity

Open paper AskPDF arXiv

Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that distant nodes effectively influence each other. Sheaf neural networks enrich graph propagation through matrix-valued transport between stalks; still, this expressivity alone does not automatically imply effective long-range communication. We introduce ONDA, a long-range graph learning framework based on operator-valued information waves. Stalk-valued representations evolve through second-order dynamics governed by learned sheaf transport operators, combining wave-like propagation with expressive local geometry. We characterize long-range influence through a stalk-wise sensitivity analysis and show that the cross-influence never vanishes. Across long-range propagation, severe graph bottlenecks, graph transfer, and heterophilic benchmarks, ONDA consistently improves over scalar wave propagation, diffusive sheaf baselines, and state-of-the-art models, demonstrating the benefit of coupling wave dynamics with matrix-valued transport.