Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Park, MoonJeong, Kim, Dongwoo |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
by: Park, MoonJeong, et al.
Published: (2024)
by: Park, MoonJeong, et al.
Published: (2024)
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
by: Park, MoonJeong, et al.
Published: (2025)
by: Park, MoonJeong, et al.
Published: (2025)
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
by: Heo, Jaeseung, et al.
Published: (2025)
by: Heo, Jaeseung, et al.
Published: (2025)
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
by: Park, MoonJeong, et al.
Published: (2026)
by: Park, MoonJeong, et al.
Published: (2026)
Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
by: Yoon, Seokwon, et al.
Published: (2026)
by: Yoon, Seokwon, et al.
Published: (2026)
In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration
by: Choi, Youngbin, et al.
Published: (2025)
by: Choi, Youngbin, et al.
Published: (2025)
CoPL: Collaborative Preference Learning for Personalizing LLMs
by: Choi, Youngbin, et al.
Published: (2025)
by: Choi, Youngbin, et al.
Published: (2025)
Local Vertex Colouring Graph Neural Networks
by: Li, Shouheng, et al.
Published: (2024)
by: Li, Shouheng, et al.
Published: (2024)
Graph Neural Networks Do Not Always Oversmooth
by: Epping, Bastian, et al.
Published: (2024)
by: Epping, Bastian, et al.
Published: (2024)
Are We Measuring Oversmoothing in Graph Neural Networks Correctly?
by: Zhang, Kaicheng, et al.
Published: (2025)
by: Zhang, Kaicheng, et al.
Published: (2025)
Generalization of Graph Neural Networks through the Lens of Homomorphism
by: Li, Shouheng, et al.
Published: (2024)
by: Li, Shouheng, et al.
Published: (2024)
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
by: Wu, Xinyi, et al.
Published: (2023)
by: Wu, Xinyi, et al.
Published: (2023)
Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks
by: Kelesis, Dimitrios, et al.
Published: (2024)
by: Kelesis, Dimitrios, et al.
Published: (2024)
On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks
by: Chehreghani, Mostafa Haghir
Published: (2026)
by: Chehreghani, Mostafa Haghir
Published: (2026)
Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
by: Keriven, Nicolas
Published: (2025)
by: Keriven, Nicolas
Published: (2025)
Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks
by: Kelesis, Dimitrios, et al.
Published: (2025)
by: Kelesis, Dimitrios, et al.
Published: (2025)
Towards Bridging Generalization and Expressivity of Graph Neural Networks
by: Li, Shouheng, et al.
Published: (2024)
by: Li, Shouheng, et al.
Published: (2024)
Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View
by: Jin, Yufei, et al.
Published: (2024)
by: Jin, Yufei, et al.
Published: (2024)
Partially Trained Graph Convolutional Networks Resist Oversmoothing
by: Kelesis, Dimitrios, et al.
Published: (2024)
by: Kelesis, Dimitrios, et al.
Published: (2024)
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2024)
by: Chakraborty, Biswadeep, et al.
Published: (2024)
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
by: Wenkel, Frederik, et al.
Published: (2022)
by: Wenkel, Frederik, et al.
Published: (2022)
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
by: Hossen, Md Sazzad, et al.
Published: (2026)
by: Hossen, Md Sazzad, et al.
Published: (2026)
Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for Text-to-Image Diffusion Model Unlearning
by: Moon, Saemi, et al.
Published: (2024)
by: Moon, Saemi, et al.
Published: (2024)
Oversmoothing: A Nightmare for Graph Contrastive Learning?
by: Li, Jintang, et al.
Published: (2023)
by: Li, Jintang, et al.
Published: (2023)
A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs
by: Wang, Jiaqi, et al.
Published: (2025)
by: Wang, Jiaqi, et al.
Published: (2025)
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
by: Qin, Zhenyue, et al.
Published: (2021)
by: Qin, Zhenyue, et al.
Published: (2021)
Feature Unlearning for Pre-trained GANs and VAEs
by: Moon, Saemi, et al.
Published: (2023)
by: Moon, Saemi, et al.
Published: (2023)
Resolving Oversmoothing with Opinion Dissensus
by: Wang, Keqin, et al.
Published: (2025)
by: Wang, Keqin, et al.
Published: (2025)
Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
by: Dönmez, Arif, et al.
Published: (2026)
by: Dönmez, Arif, et al.
Published: (2026)
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
by: Sun, Li, et al.
Published: (2025)
by: Sun, Li, et al.
Published: (2025)
Shared-Weights Extender and Gradient Voting for Neural Network Expansion
by: Chatzis, Nikolas, et al.
Published: (2025)
by: Chatzis, Nikolas, et al.
Published: (2025)
Accelerating Storage-Based Training for Graph Neural Networks
by: Jang, Myung-Hwan, et al.
Published: (2026)
by: Jang, Myung-Hwan, et al.
Published: (2026)
Setting the Record Straight on Transformer Oversmoothing
by: Dovonon, Gbètondji J-S, et al.
Published: (2024)
by: Dovonon, Gbètondji J-S, et al.
Published: (2024)
Posterior Label Smoothing for Node Classification
by: Heo, Jaeseung, et al.
Published: (2024)
by: Heo, Jaeseung, et al.
Published: (2024)
Long Live the Librarian! A Persistent Search Sub-Agent for Energy-Efficient Multi-Agent Software Engineering Systems
by: Cho, Seunghyuk, et al.
Published: (2026)
by: Cho, Seunghyuk, et al.
Published: (2026)
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering
by: Li, Shouheng, et al.
Published: (2022)
by: Li, Shouheng, et al.
Published: (2022)
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
by: Hossain, Tanvir, et al.
Published: (2024)
by: Hossain, Tanvir, et al.
Published: (2024)
Graph-based Integrated Gradients for Explaining Graph Neural Networks
by: Simpson, Lachlan, et al.
Published: (2025)
by: Simpson, Lachlan, et al.
Published: (2025)
Graph Generation with $K^2$-trees
by: Jang, Yunhui, et al.
Published: (2023)
by: Jang, Yunhui, et al.
Published: (2023)
Training Robust Graph Neural Networks by Modeling Noise Dependencies
by: In, Yeonjun, et al.
Published: (2025)
by: In, Yeonjun, et al.
Published: (2025)
Similar Items
-
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
by: Park, MoonJeong, et al.
Published: (2024) -
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
by: Park, MoonJeong, et al.
Published: (2025) -
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
by: Heo, Jaeseung, et al.
Published: (2025) -
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
by: Park, MoonJeong, et al.
Published: (2026) -
Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
by: Yoon, Seokwon, et al.
Published: (2026)