GeoFlow improves OD flow prediction and generation by augmenting area representations with geospatial attributes and using a geometric-intrinsic fusion encoder with axial-global attention decoder.
On Measuring Long-Range Interactions in Graph Neural Networks
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Long-range graph tasks -- those dependent on interactions between distant nodes -- are an open problem in graph neural network research. Real-world benchmark tasks, especially the Long Range Graph Benchmark, have become popular for validating the long-range capability of proposed architectures. However, this is an empirical approach that lacks both robustness and theoretical underpinning; a more principled characterization of the long-range problem is required. To bridge this gap, we formalize long-range interactions in graph tasks, introduce a range measure for operators on graphs, and validate it with synthetic experiments. We then leverage our measure to examine commonly used tasks and architectures, and discuss to what extent they are, in fact, long-range. We believe our work advances efforts to define and address the long-range problem on graphs, and that our range measure will aid evaluation of new datasets and architectures.
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2026 3roles
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Introduces Ramanujan Propagation as a graph rewiring method for GNNs that leverages Ramanujan graphs to ensure non-negative resistance curvature while preserving local connectivity and outperforming prior rewiring techniques.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation
GeoFlow improves OD flow prediction and generation by augmenting area representations with geospatial attributes and using a geometric-intrinsic fusion encoder with axial-global attention decoder.
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Ramanujan Graph Rewiring with Non Negative Resistance Curvature
Introduces Ramanujan Propagation as a graph rewiring method for GNNs that leverages Ramanujan graphs to ensure non-negative resistance curvature while preserving local connectivity and outperforming prior rewiring techniques.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.