Cover Image

A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Jiaqi, Wu, Xinyi, Cheng, James, Wang, Yifei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs
by: Scholkemper, Michael, et al.
Published: (2024)

Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
by: Park, MoonJeong, et al.
Published: (2024)

Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
by: Zhao, Weichen, et al.
Published: (2024)

Demystifying Oversmoothing in Attention-Based Graph Neural Networks
by: Wu, Xinyi, et al.
Published: (2023)

Solving Oversmoothing in GNNs via Nonlocal Message Passing: Algebraic Smoothing and Depth Scalability
by: Guan, Weiqi, et al.
Published: (2025)

Oversmoothing: A Nightmare for Graph Contrastive Learning?
by: Li, Jintang, et al.
Published: (2023)

Resolving Oversmoothing with Opinion Dissensus
by: Wang, Keqin, et al.
Published: (2025)

Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
by: Hossain, Tanvir, et al.
Published: (2024)

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
by: Ma, Jiangyan, et al.
Published: (2023)

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
by: Errica, Federico, et al.
Published: (2023)

Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
by: Park, MoonJeong, et al.
Published: (2024)

Partially Trained Graph Convolutional Networks Resist Oversmoothing
by: Kelesis, Dimitrios, et al.
Published: (2024)

Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
by: Hossen, Md Sazzad, et al.
Published: (2026)

Mitigating Degree Bias in Signed Graph Neural Networks
by: He, Fang, et al.
Published: (2024)

Are We Measuring Oversmoothing in Graph Neural Networks Correctly?
by: Zhang, Kaicheng, et al.
Published: (2025)

Graph Neural Networks Do Not Always Oversmooth
by: Epping, Bastian, et al.
Published: (2024)

Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
by: Keriven, Nicolas
Published: (2025)

Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
by: Sun, Li, et al.
Published: (2025)

On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks
by: Chehreghani, Mostafa Haghir
Published: (2026)

Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks
by: Kelesis, Dimitrios, et al.
Published: (2024)

Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View
by: Jin, Yufei, et al.
Published: (2024)

Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks
by: Kelesis, Dimitrios, et al.
Published: (2025)

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
by: Attali, Hugo, et al.
Published: (2024)

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
by: Attali, Hugo, et al.
Published: (2026)

Setting the Record Straight on Transformer Oversmoothing
by: Dovonon, Gbètondji J-S, et al.
Published: (2024)

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
by: Wenkel, Frederik, et al.
Published: (2022)

Rethinking the Expressive Power of GNNs via Graph Biconnectivity
by: Zhang, Bohang, et al.
Published: (2023)

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
by: Park, MoonJeong, et al.
Published: (2025)

On the Scalability of GNNs for Molecular Graphs
by: Sypetkowski, Maciej, et al.
Published: (2024)

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2024)

Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning
by: Zhang, Yifei, et al.
Published: (2023)

Understanding GNNs and Homophily in Dynamic Node Classification
by: Ito, Michael, et al.
Published: (2025)

Locality-Aware Graph-Rewiring in GNNs
by: Barbero, Federico, et al.
Published: (2023)

GSR-GNN: Training Acceleration and Memory-Saving Framework of Deep GNNs on Circuit Graph
by: Luo, Yuebo, et al.
Published: (2026)

MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
by: Yao, Tianjun, et al.
Published: (2024)

GraphBridge: Towards Arbitrary Transfer Learning in GNNs
by: Ju, Li, et al.
Published: (2025)

Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
by: Luo, Yuankai, et al.
Published: (2024)

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
by: Wang, Kun, et al.
Published: (2024)

Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulations
by: Seo, Sangwoo, et al.
Published: (2025)

Acceleration Algorithms in GNNs: A Survey
by: Ma, Lu, et al.
Published: (2024)