Oversmoothing

Latest papers

14 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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
Does Graph Compression Preserve Signal Propagation?
Jul 25, 2026Kawshik Banerjee, Khaled Mohammed SaifuddinGraph Signal Processing Graph Coarsening
School of Computing, Southern Illinois University, Carbondale, IL, USA
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
Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphs
Jul 15, 2026Mohammad ForouheshOversmoothing Inverse Problems
Amirkabir University of Technology, Iran
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
A Spectral Theory of Normalized Corrected GNN Propagation
Jun 22, 2026Qihan Chen, Wei Li, Meng Qin +1Graph Neural Networks Spectral Methods
Center for Discrete Mathematics, Fuzhou University · School of Computer and Information Science, Fujian Agriculture and Forestry University · Department of Strategic & Advanced Interdisciplinary Research, Pengcheng Laboratory (PCL), Shenzhen, Guangdong, China
Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Jun 22, 2026Xinxi Chen, Junyang Chen, Yiqun Zhang +2Graph Structure Learning Graph Neural Networks
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Deep Neural Sheaf Diffusion
May 18, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorGraph Neural Networks Sheaf Neural Networks
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Scalable Gaussian process inference via neural feature maps
May 11, 2026Anthony StephensonGaussian Processes Kernel Methods
School of Mathematics University of Bristol
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.
Sink vs. diagonal patterns as mechanisms for attention switch and oversmoothing prevention
May 8, 2026Peter Súkeník, Cristina López Amado, Christoph H. Lampert +1Self-Attention Transformer Attention
Institute of Science and Technology (ISTA) Austria
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
Avoiding Over-smoothing in Social Media Rumor Detection with Pre-trained Propagation Tree Transformer
Mar 24, 2026Chaoqun Cui, Caiyan JiaOversmoothing Fake News Detection
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing 100044, China
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

Aug 17, 2026J/K move · Enter open · S save

Demystifying Oversmoothing in Sheaf Neural Networks: An Index-Theoretic Criterion

Junwen Dong, Yuhan Peng, Hao Li, Huitao Feng+1

Department of Mathematics, Faculty of Science National University of Singapore Singapore · School of Physical and Mathematical Sciences Nanyang Technological University Singapore

GNN Oversmoothing Sheaf Neural Networks Over-Smoothing in GNNs Oversmoothing Graph Neural Networks

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To combat oversmoothing in Graph Convolutional Networks, Sheaf Neural Networks (SNNs) were proposed as a generalization by equipping the graph with a sheaf structure and replacing the graph Laplacian with a sheaf Laplacian

\mathcal{L}

. Existing analyses connect sheaf diffusion to oversmoothing via the harmonic space (

\ker\mathcal{L}

), taking its absolute dimension as an indicator of anti-oversmoothing capacity. However, absolute dimension alone is not a reliable measure: certain sheaf configurations inflate

\dim \ker \mathcal{L}

while their harmonic sections remain entirely constant, without enriching discriminative capacity. We instead introduce the first relative, geometric approach, yielding a precise characterisation of anti-oversmoothing capacity. Under natural conditions on stalk transportation and global sheaf structure, we establish an index-theoretic comparison criterion showing that one sheaf's harmonic space genuinely contains another's beyond trivial inflation. We illustrate this with a concrete instance and further introduce GyroSheaf, a sheaf with curved gyrovector-space stalks, extending the criterion to the non-linear setting via local tangent-space linearization. Experiments across ten models confirm the theoretical criterion: sheaf models violating the criterion collapse despite possessing index jumps, while compliant models maintain depth-stable representations.