Over-Smoothing in GNNs

GNN: Graph Neural Network

Latest papers

21 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Stability-Shaped Deep Graph Learning
Oct 5, 2026Junyou Zhu, Langzhou He, Fenying Cai +7Graph Neural Networks Over-Smoothing in GNNs
Department of Complexity Science, Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany · Machine Learning Group, Technical University of Berlin, 10587 Berlin, Germany · Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA +6
Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations
Sep 12, 2026Mostafa Haghir ChehreghaniOver-Smoothing in GNNs Recurrent GNNs
Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran, Iran
Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks
Sep 6, 2026Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1Over-Smoothing in GNNs Graph Neural Networks
Department of Mechanical Engineering University of Melbourne Melbourne, Australia · School of Computing Australian National University Canberra, Australia · Data61, CSIRO Canberra, Australia
Demystifying Oversmoothing in Sheaf Neural Networks: An Index-Theoretic Criterion
Aug 17, 2026Junwen Dong, Yuhan Peng, Hao Li +2GNN Oversmoothing Sheaf Neural Networks
Department of Mathematics, Faculty of Science National University of Singapore Singapore · School of Physical and Mathematical Sciences Nanyang Technological University Singapore
Exploring Oversmoothing with Householder Matrices
Aug 12, 2026Bhaskar KarolGraph Neural Networks GNN Oversmoothing
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
Persistent Gaussian Perturbations Prevent Oversmoothing in Recurrent Graph Neural Networks
Jul 30, 2026Mostafa Haghir ChehreghaniGraph Neural Networks Stochastic Linear Dynamical Systems
Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran, Iran
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
From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks
Jul 17, 2026Zhiheng Zhou, Mengyao Zhou, Yancheng Chen +3Hypergraph Neural Networks Hypergraph Learning
School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences and also with the University of Chinese Academy of Sciences, Beijing 100190, China
Graph Navier Stokes Networks
May 20, 2026Zexing Zhao, Guangsi Shi, Yu Gong +4Graph Neural Networks GNN Oversmoothing
Northwest A&F University Yangling, Shaanxi, China · Corporate Research Center, Midea Group Shanghai, China · Bosch Shanghai, China
Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
May 11, 2026Arif Dönmez, Axel Mosig, Ellen Fritsche +1Graph Neural Networks Sheaf Neural Networks
IUF – Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany · DNTOX GmbH, Düsseldorf, Germany · Bioinformatics Group, Ruhr University Bochum, Bochum, Germany +1
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.
Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learning
Apr 13, 2026Abeer Mostafa, Raneen Younis, Zahra AhmadiOver-Smoothing in GNNs Graph Neural Networks
Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School, Hannover, Germany · Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed), Hannover, Germany
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
Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach
Mar 16, 2026Mengyao Zhou, Zhiheng Zhou, Xiao Han +3Over-Smoothing in GNNs Hypergraph Neural Networks
the Academy of Mathematics and Systems Science, Chinese Academy of Sciences and also with the University of Chinese Academy of Sciences, Beijing 100190, China · the School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, China · School of Artificial Intelligence, Beihang University, Beijing 100191, China +1
On the Necessity of Learnable Sheaf Laplacians
Mar 5, 2026Ferran Hernandez Caralt, Mar Gonzàlez i Català, Adrián Bazaga +1GNN Oversmoothing Sheaf Neural Networks
Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom · Microsoft
Invariant-Stratified Propagation for Expressive Graph Neural Networks
Mar 2, 2026Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1Over-Smoothing in GNNs Higher-Order GNNs
Department of Mechanical Engineering, University of Melbourne, Melbourne, Australia · School of Computing, Australian National University, Canberra, Australia · Data61, CSIRO, Canberra, Australia
Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
Feb 17, 2026Erkan Turan, Gaspard Abel, Maysam Behmanesh +2GNN Oversmoothing Graph Neural Networks
LIX, Ecole Polytechnique, IP Paris · Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190, Gif-sur-Yvette, France · Centre d’Analyse et de Mathématique Sociales, EHESS, CNRS, 75006 Paris, France..
Smoothness Errors in Dynamics Models and How to Avoid Them
Feb 5, 2026Edward Berman, Luisa Li, Jung Yeon Park +1Graph Neural Networks Over-Smoothing in GNNs
Geometric Learning Lab, Northeastern University.
Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
Oct 6, 2025Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov +1Over-Smoothing in GNNs Graph-Based Semi-Supervised Learning
Graph Unitary Message Passing
Mar 17, 2024Haiquan Qiu, Quanming YaoGraph Neural Networks Message Passing Neural Networks
Department of Electrical Engineering, Tsinghua University · 2Beijing National Research Center for Information Science and Technology · 3State Key laboratory of Space Network and Communications

Oct 5, 2026J/K move · Enter open · S save

Stability-Shaped Deep Graph Learning

Junyou Zhu, Langzhou He, Fenying Cai, Christian Nauck+6

Department of Complexity Science, Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany · Machine Learning Group, Technical University of Berlin, 10587 Berlin, Germany · Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA+6

Graph Neural Networks Over-Smoothing in GNNs GNN Oversmoothing

Open paper AskPDF arXiv

In deep graph neural networks, increasing depth enlarges the receptive field but often leads to over-smoothing, where node representations tend to align. We develop a unified, mode-wise stability framework for deep GNN propagation that provides a principled characterization of over-smoothing. By interpreting layer depth as time and layer updates as graph-coupled dynamics, over-smoothing can be understood as an undesirable dynamical synchronization of features, for which the master stability curve provides a theoretical tool to assess the stability of synchrony. Guided by this theory, we further propose Stability-Shaped Deep Graph Learning (SDGL) to mitigate over-smoothing in deep GNNs. SDGL has two complementary instantiations: one induces controlled Turing instability to replace synchronization with spatial pattern formation, and the other maintains stable near-critical propagation. Experiments on diverse node- and graph-level benchmarks demonstrate the improved depth scaling and consistent accuracy gains over strong baselines, including graphs exhibiting long-range dependencies.