GNN Expressivity

GNN: Graph Neural Network

Follow this topic Ask about this topic

Momentum

6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 36

All topics

Oscillatory Neural Dynamics over Sheaves
Oct 7, 2026Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina +5Graph Neural Networks Graph Representation Learning
FaBiT, University of Bologna · Department of Computer Science, University of Pisa · Department of Computer, Control and Management Engineering, Sapienza University of Rome +1
Multi-Attractor GNNs: Set-Valued Expressivity Beyond Unique Equilibria
Sep 28, 2026Jialin LiuGNN Expressivity Combinatorial Optimization
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction
Sep 28, 2026Chen Shao, Donald Loveland, Tobias Käfer +1Link Prediction GNN Expressivity
Karlsruhe Institute of Technology Karlsruhe, Germany · Department of Computer Science University of Michigan Ann Arbor, MI, USA · Computer Science & Engineering University of Michigan Ann Arbor, MI, USA
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
Repurposing Unified Topological Signatures for Graph Representation Learning
Sep 15, 2026Sanyam Sanjay Jain, Anshika Krishnatray, Aditya Sharma +1Graph Representation Learning GNN Expressivity
BITS Pilani, India
On the Expressive Power of Implicit Line-Graph Higher-Order Weisfeiler--Leman
Sep 14, 2026Fan YangGNN Expressivity
Independent Researcher
Recurrent GraphNeural NetworkswithSet-BasedAggregation
Sep 14, 2026Blai BonetGraph Neural Networks GNN Expressivity
Universidad Simón Bolívar Caracas, Venezuela
Edge-Girth as a Structural Edge Feature for Graph Neural Networks
Sep 1, 2026Lilian Marey, Charlotte LaclauGraph Neural Networks Graph Representation Learning
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France
Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials
Sep 1, 2026Pingbing Ming, Han WangMachine Learning Interatomic Potentials GNN Expressivity
SKLMS, Institute of Computational Mathematics and Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences, No. 55, East Road Zhong-Guan-Cun, Beijing 100190, China · National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, P.R. China, and HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P.R. China
The Boolean Power of ReLU
Aug 12, 2026Pablo Barceló, Floris Geerts, Matthias Lanzinger +2ReLU Neural Networks GNN Expressivity
Pontifical Catholic University · IMFD & CENIA, Chile · University of Antwerp +4
When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series
Aug 7, 2026Chen Shao, Yue Wang, Zhenyi Zhu +4Multivariate Time Series Forecasting Temporal GNNs
Karlsruhe Institute of Technology, Germany · The Hong Kong University of Science and Technology, Hong Kong SAR · East China Normal University, China +1
Tropical Algebraic Geometry for Neuronal Representations: An Arakelov-Green Measure Based Descriptor for Graph Learning
Aug 5, 2026Yuyang Zhang, Weihan Xu, Xuehai Zhou +2Graph Representation Learning GNN Expressivity
McGill University
Designing a Good Virtual Node: Addressable and Cardinality-Preserving Global Memory for Message Passing Architectures
Aug 3, 2026Félix MarcocciaCross-Attention GNN Expressivity
Universality and Approximation Rates of Graph Neural Networks with Random Features
Jul 29, 2026Lukas Gonon, Thilo Meyer-Brandis, Niklas WeberGraph Neural Networks Equivariant Neural Networks
School of Computer Science University of St. Gallen St. Gallen, 9000, Switzerland · Department of Mathematics LMU Munich Munich, 80333, Germany
CondPSE: A Polynomial-Filtered Structural Encoder with Conditional Modulation for Graphs
Jul 28, 2026Woohyun Lee, Hogun ParkGraph Representation Learning Graph SSL
Sungkyunkwan University Suwon, Republic of Korea
A Weisfeiler-Leman Characterization of Global-Attention Graph Transformers for Mixed-Integer Linear Programs
Jul 20, 2026Md Abrar Jahin, Craig A. Knoblock, Jay PujaraMixed-Integer Linear Programming GNN Expressivity
University of Southern California, Los Angeles, CA 90089, USA · USC Information Sciences Institute, Marina del Rey, CA 90292, USA
Scaling Weisfeiler-Leman Expressiveness Analysis to Massive Graphs with GPUs
Jul 1, 2026Filippo Biondi, Mirco Tribastone, Max TschaikowskiGPU Acceleration High-Performance Computing
IMT Lucca, Italy · Sapienza University Rome, Italy
Graph Neural Networks Applications Across Domains: All Insights You Need
Jun 25, 2026Abderaouf BahiGraph Neural Networks Over-Squashing
Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, P.O. Box 73, El Tarf 36000, Algeria
Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
Jun 25, 2026Snehal Raj, Brian Coyle, Léo Monbroussou +3GNN Expressivity Message Passing Neural Networks
1LIP6, CNRS, Sorbonne Universit´e, Paris, France · 2QC Ware, Palo Alto, USA and Paris, France · School of Informatics, University of Edinburgh, Edinburgh, UK +3
Structural Preservation and the Logical Expressiveness of Graph Neural Networks
Jun 16, 2026Przemysław Andrzej Wałęga, Bernardo Cuenca GrauGNN Expressivity
Queen Mary University of London · University of Oxford
Understanding Truncated Positional Encodings for Graph Neural Networks
Jun 11, 2026James Flora, Mitchell Black, Weng-Keen Wong +1GNN Expressivity Positional Encoding
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA · Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, California, USA.
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Jun 2, 2026Li Sun, Zhenhao Huang, Yiding Wang +3GNN Expressivity Geometric Representation Learning
Beijing University of Posts and Telecommunications, Beijing, China · North China Electric Power University, Beijing, China · University of Cambridge, Cambridge, UK +1
Temporal Motif Signatures for Temporal Graph Neural Networks
May 31, 2026Dylan Sandfelder, Mihai Cucuringu, Xiaowen DongTemporal GNNs GNN Expressivity
University of Oxford · University of California, Los Angeles
Invariant-Based Weight Sharing for Message Passing
May 25, 2026Florian SeiffarthGraph Neural Networks GNN Expressivity
University of Bonn, Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
May 25, 2026Nil Ayday, Mahalakshmi Sabanayagam, Debarghya GhoshdastidarNeural Network Generalization Graph Neural Networks
Technical University of Munich, School of Computation, Information and Technology, Boltzmannstr. 3, Garching, 85748, Germany. · Australian Institute for Machine Learning, Adelaide University,2026 Adelaide, SA 5000, Australia.
Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
May 22, 2026Snir Hordan, Nadav Dym, Tim SeppeltGraph Neural Networks GNN Expressivity
Faculty of Mathematics Technion - Israel Institute of Technology · IT University of Copenhagen
Expressive Power of Deep Homomorphism Networks over Relational Databases
May 18, 2026Moritz Schönherr, Balder ten Cate, Maurice Funk +3Logical Reasoning Graph Neural Networks
Leipzig University · University of Amsterdam · Technion & RelationalAI
The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks
May 13, 2026Fengqing Jiang, Yuetai Li, Yichen Feng +6GNN Expressivity Hypergraph Neural Networks
University of Washington · Western Washington University · King Abdulaziz City for Science and Technology +1
MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing
May 13, 2026Chaokai Wu, Haofu Shi, Ningxuan Ma +2Multi-Label Classification Graph Neural Networks
Full-Spectrum Graph Neural Networks: Expressive and Scalable
May 7, 2026Xiaohan Wang, Deyu Bo, Longlong Li +1Graph Neural Networks GNN Expressivity
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.

Oct 7, 2026, cs.LGJ/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

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.

Momentum

Related topics

Latest papers 36