oversmoothing

Understanding Oversmoothing in Graph Neural Networks (GNNs): Insights from Two Theoretical Studies

KDD 2025 - Understanding Oversmoothing in Diffusion-Based GNNs

[ICML 2024] Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs

Part 13: measuring and relieving the oversmoothing problem for graph neural networks...

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

[CoLoRAI 25] A Low-Rank Perspective on Oversmoothing in Graph Neural Networks

110320_Oversmoothing of GNNs and its Solutions

Part 7: on information dropping and oversmoothing in graph neural networks

Part 15: A note on oversmoothing for graph neural networks

AMMI 2022 Course 'Geometric Deep Learning' - Seminar 4 (Neural Sheaf Diffusion) - Cristian Bodnar

AI Research Paper Overview: Spectral Graph Pruning Against Over-Squashing and Over-Smoothing

Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning

Part 17: pairnorm: tackling oversmoothing in GNNs

Part 105: adaptive message passing: a general framework to mitigate oversmoothing, oversquashing...

Part 18: understanding virtual nodes: oversmoothing, oversquashing and node hetetogeneity

Part 48: tackling oversmoothing in GNN via graph sparsification

Part 6: shedding light on random dropping and oversmoothing

Graph-Coupled Oscillator Networks | T. Konstantin Rusch

Designing Efficient Neural Networks for PDEs Using Fast Boundary Element Methods

UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in

On the Bottleneck of Graph Neural Networks and its Practical Implications | Authors explain ML Paper

UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in

MOOSComp: Improving Lightweight Long-Context Compressor via Mitigating Over-Smoothing and Incorpor

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