Over-Squashing in GNNs

GNN: Graph Neural Network

Follow this topic Ask about this topic

Latest papers

16 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective
Oct 3, 2026André Ribeiro, Germano Barcelos, Amauri H. Souza +2Over-Squashing in GNNs Graph Neural Networks
Getulio Vargas Foundation · Federal University of Viçosa · Federal Institute of Ceará, 2δ AI +2
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
Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment
Aug 11, 2026Yan Wang, Chuan-Xian RenGraph Rewiring Over-Squashing
School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN
Aug 10, 2026Killian Cressant, Pedro B. VellosoGraph Neural Networks Over-Squashing in GNNs
CEDRIC Lab, Conservatoire National des Arts et M´etiers (Cnam), 75003 Paris, France
Schreier-Coset Graph Rewiring
Jul 29, 2026Aryan Mishra, Randy Martinez, Lizhen LinGraph Neural Networks Graph Rewiring
Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature
Jul 24, 2026Rachid Caich, Yassine AbbahaddouOversmoothing Graph Rewiring
LIX, École Polytechnique IP Paris Palaiseau, France
RADE: Random Add-Drop Edge as a Regularizer
May 30, 2026Danial Saber, Amirali Salehi-AbariNeural Network Generalization Graph Neural Networks
Ontario Tech University, Oshawa, Ontario, Canada.
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
Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
May 13, 2026Eden Nagar, Ya-Wei Eileen Lin, Ron LevieGraph Neural Networks Over-Squashing in GNNs
Technion - Israel Institute of Technology, Faculty of Mathematics · Technical University of Munich, School of Computation, Information and Technology · Munich Center for Machine Learning
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
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
May 8, 2026Md Sazzad Hossen, Avimanyu SahooGraph Heterophily Over-Squashing
University of Alabama in Huntsville, Huntsville, AL, USA.
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
May 1, 2026Hugo Attali, Nathalie Pernelle, Davide Buscaldi +1Graph Neural Networks Graph Rewiring
Universit´e Sorbonne Paris Nord, CNRS, LIPN · Universit´e Paris-Saclay, CentraleSup´elec, Inria
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Mar 29, 2026Tanvir Hossain, Muhammad Ifte Khairul Islam, Lilia Chebbah +2Over-Squashing in GNNs Graph Neural Networks
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA · Department of Data Science and Analytics, Kennesaw State University, 1000 Chastain Road, Kennesaw, GA 30144, USA
On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks
Mar 27, 2026Mostafa Haghir ChehreghaniOver-Squashing in GNNs Over-Smoothing in GNNs
Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing
Mar 12, 2026Bertran Miquel-Oliver, Manel Gil-Sorribes, Victor Guallar +1Oversmoothing Graph Neural Networks
Barcelona Supercomputing Center (BSC), Barcelona, Spain · Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain · Nostrum Biodiscovery, Barcelona, 08029, Spain +1
Understanding over-squashing and bottlenecks on graphs via curvature
Nov 29, 2021Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain +2Graph Rewiring Over-Squashing
University of Oxford · Imperial College London · Twitter

Oct 3, 2026J/K move · Enter open · S save

Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective

André Ribeiro, Germano Barcelos, Amauri H. Souza, Diego Mesquita+1

Getulio Vargas Foundation · Federal University of Viçosa · Federal Institute of Ceará, 2δ AI+2

Over-Squashing in GNNs Graph Neural Networks Sheaf Neural Networks Graph Diffusion

Open paper AskPDF arXiv

Graph Neural Networks (GNNs) often struggle to capture long-range dependencies due to over-squashing -- a phenomenon in which the repeated compression of node embeddings into finite-size messages causes representations to collapse. Over-squashing is most often diagnosed as a property of the graph topology, with effective resistance serving as a principled measure of the bottleneck. We provide a complementary view on the matter: building on cellular sheaves, we introduce sheaf effective resistance, a generalization of effective resistance that depends on the sheaf attached to the graph, and we prove that for flat vector bundles, the over-squashing sensitivity in the Jacobian sense is upper bounded by a quantity related to the sheaf effective resistance between the nodes. The bottleneck thus need not lie in the graph itself: it can be relocated, and reduced, by adjusting the sheaf. We instantiate this idea in FlatNSD, a simple message-passing variant of Neural Sheaf Diffusion, and show that it implicitly learns to modulate total sheaf effective resistance, performing well on benchmarks designed to stress over-squashing without altering the original graph topology.