Saved in:
| Main Authors: | Honor, Samuel, Abdelnaby, Mohamed, Leahy, Kevin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.02615 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging
by: Abdelnaby, Mohamed, et al.
Published: (2026)
by: Abdelnaby, Mohamed, et al.
Published: (2026)
Universally Invariant Learning in Equivariant GNNs
by: Cen, Jiacheng, et al.
Published: (2025)
by: Cen, Jiacheng, et al.
Published: (2025)
Are GNNs doomed by the topology of their input graph?
by: Aboussalah, Amine Mohamed, et al.
Published: (2025)
by: Aboussalah, Amine Mohamed, et al.
Published: (2025)
Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
by: Azzolin, Steve, et al.
Published: (2024)
by: Azzolin, Steve, et al.
Published: (2024)
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs
by: Chishtie, Farrukh A., et al.
Published: (2024)
by: Chishtie, Farrukh A., et al.
Published: (2024)
What Expressivity Theory Misses: Message Passing Complexity for GNNs
by: Kemper, Niklas, et al.
Published: (2025)
by: Kemper, Niklas, et al.
Published: (2025)
Rotational Sampling: A Plug-and-Play Encoder for Rotation-Invariant 3D Molecular GNNs
by: Jin, Dian
Published: (2025)
by: Jin, Dian
Published: (2025)
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
by: Luo, Yuankai, et al.
Published: (2024)
by: Luo, Yuankai, et al.
Published: (2024)
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
by: Guo, Kai, et al.
Published: (2024)
by: Guo, Kai, et al.
Published: (2024)
Redesigning graph filter-based GNNs to relax the homophily assumption
by: Rey, Samuel, et al.
Published: (2024)
by: Rey, Samuel, et al.
Published: (2024)
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
by: Ma, Jiangyan, et al.
Published: (2023)
by: Ma, Jiangyan, et al.
Published: (2023)
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
by: Hayderi, Alexandre, et al.
Published: (2024)
by: Hayderi, Alexandre, et al.
Published: (2024)
Structure-Guided Input Graph for GNNs facing Heterophily
by: Tenorio, Victor M., et al.
Published: (2024)
by: Tenorio, Victor M., et al.
Published: (2024)
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
by: Brown, Gavin, et al.
Published: (2023)
by: Brown, Gavin, et al.
Published: (2023)
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
by: Khaled, Qusai, et al.
Published: (2026)
by: Khaled, Qusai, et al.
Published: (2026)
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
by: Seo, Hyunjin, et al.
Published: (2024)
by: Seo, Hyunjin, et al.
Published: (2024)
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
by: Yi, Lu, et al.
Published: (2025)
by: Yi, Lu, et al.
Published: (2025)
Certified Robust Invariant Polytope Training in Neural Controlled ODEs
by: Harapanahalli, Akash, et al.
Published: (2024)
by: Harapanahalli, Akash, et al.
Published: (2024)
Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing
by: Liu, Wenyi, et al.
Published: (2024)
by: Liu, Wenyi, et al.
Published: (2024)
On the Scalability of GNNs for Molecular Graphs
by: Sypetkowski, Maciej, et al.
Published: (2024)
by: Sypetkowski, Maciej, et al.
Published: (2024)
Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling
by: Hwang, Himchan, et al.
Published: (2025)
by: Hwang, Himchan, et al.
Published: (2025)
Mean-Field Control on Sparse Graphs: From Local Limits to GNNs via Neighborhood Distributions
by: Schmidt, Tobias, et al.
Published: (2026)
by: Schmidt, Tobias, et al.
Published: (2026)
Graph Q-Learning for Combinatorial Optimization
by: Dax, Victoria M., et al.
Published: (2024)
by: Dax, Victoria M., et al.
Published: (2024)
Generalizing GNNs with Tokenized Mixture of Experts
by: Guo, Xiaoguang, et al.
Published: (2026)
by: Guo, Xiaoguang, et al.
Published: (2026)
Locality-Aware Graph-Rewiring in GNNs
by: Barbero, Federico, et al.
Published: (2023)
by: Barbero, Federico, et al.
Published: (2023)
VIBR: Learning View-Invariant Value Functions for Robust Visual Control
by: Dupuis, Tom, et al.
Published: (2023)
by: Dupuis, Tom, et al.
Published: (2023)
From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
by: Lina, Debolina Halder, et al.
Published: (2026)
by: Lina, Debolina Halder, et al.
Published: (2026)
On the Expressive Power of GNNs to Solve Linear SDPs
by: Qian, Chendi, et al.
Published: (2026)
by: Qian, Chendi, et al.
Published: (2026)
Fixed Aggregation Features Can Rival GNNs
by: Rubio-Madrigal, Celia, et al.
Published: (2026)
by: Rubio-Madrigal, Celia, et al.
Published: (2026)
On the Limitation and Experience Replay for GNNs in Continual Learning
by: Su, Junwei, et al.
Published: (2023)
by: Su, Junwei, et al.
Published: (2023)
'Hello, World!': Making GNNs Talk with LLMs
by: Kim, Sunwoo, et al.
Published: (2025)
by: Kim, Sunwoo, et al.
Published: (2025)
Understanding GNNs and Homophily in Dynamic Node Classification
by: Ito, Michael, et al.
Published: (2025)
by: Ito, Michael, et al.
Published: (2025)
Expressivity and Generalization: Fragment-Biases for Molecular GNNs
by: Wollschläger, Tom, et al.
Published: (2024)
by: Wollschläger, Tom, et al.
Published: (2024)
Distribution Shift Is Key to Learning Invariant Prediction
by: Zheng, Hong, et al.
Published: (2026)
by: Zheng, Hong, et al.
Published: (2026)
Out-of-Distribution Optimality of Invariant Risk Minimization
by: Toyota, Shoji, et al.
Published: (2023)
by: Toyota, Shoji, et al.
Published: (2023)
Invariant Graph Transformer for Out-of-Distribution Generalization
by: Liao, Tianyin, et al.
Published: (2025)
by: Liao, Tianyin, et al.
Published: (2025)
Robust Invariant Representation Learning by Distribution Extrapolation
by: Yoshida, Kotaro, et al.
Published: (2025)
by: Yoshida, Kotaro, et al.
Published: (2025)
PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
by: Ockerman, Seth, et al.
Published: (2025)
by: Ockerman, Seth, et al.
Published: (2025)
On the Expressive Power of GNNs for Boolean Satisfiability
by: Peltonen, Saku, et al.
Published: (2026)
by: Peltonen, Saku, et al.
Published: (2026)
Migrate Demographic Group For Fair GNNs
by: Hu, YanMing, et al.
Published: (2023)
by: Hu, YanMing, et al.
Published: (2023)
Similar Items
-
Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging
by: Abdelnaby, Mohamed, et al.
Published: (2026) -
Universally Invariant Learning in Equivariant GNNs
by: Cen, Jiacheng, et al.
Published: (2025) -
Are GNNs doomed by the topology of their input graph?
by: Aboussalah, Amine Mohamed, et al.
Published: (2025) -
Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
by: Azzolin, Steve, et al.
Published: (2024) -
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs
by: Chishtie, Farrukh A., et al.
Published: (2024)