Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Kelesis, Dimitrios, Fotakis, Dimitris, Paliouras, Georgios |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Partially Trained Graph Convolutional Networks Resist Oversmoothing
by: Kelesis, Dimitrios, et al.
Published: (2024)
by: Kelesis, Dimitrios, et al.
Published: (2024)
Tree-based Focused Web Crawling with Reinforcement Learning
by: Kontogiannis, Andreas, et al.
Published: (2021)
by: Kontogiannis, Andreas, et al.
Published: (2021)
Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
by: Park, MoonJeong, et al.
Published: (2024)
by: Park, MoonJeong, et al.
Published: (2024)
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
by: Bougiatiotis, Konstantinos, et al.
Published: (2024)
by: Bougiatiotis, Konstantinos, 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)
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
by: Wu, Xinyi, et al.
Published: (2023)
by: Wu, Xinyi, et al.
Published: (2023)
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)
Focused PU learning from imbalanced data
by: Zavitsanos, Elias, et al.
Published: (2026)
by: Zavitsanos, Elias, et al.
Published: (2026)
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)
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
by: Kliachkin, Andrii, et al.
Published: (2024)
by: Kliachkin, Andrii, 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)
A Query-Driven Approach to Space-Efficient Range Searching
by: Fotakis, Dimitris, et al.
Published: (2025)
by: Fotakis, Dimitris, et al.
Published: (2025)
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)
Efficient Parameter Estimation of Truncated Boolean Product Distributions
by: Fotakis, Dimitris, et al.
Published: (2020)
by: Fotakis, Dimitris, et al.
Published: (2020)
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
by: Kavouras, Loukas, et al.
Published: (2024)
by: Kavouras, Loukas, 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)
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)
CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks
by: Akhtar, Mushir, et al.
Published: (2026)
by: Akhtar, Mushir, 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)
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
by: Park, MoonJeong, et al.
Published: (2024)
by: Park, MoonJeong, et al.
Published: (2024)
Improved Bounds for Online Facility Location with Predictions
by: Fotakis, Dimitris, et al.
Published: (2021)
by: Fotakis, Dimitris, et al.
Published: (2021)
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)
by: Si, Chenhao, et al.
Published: (2024)
by: Si, Chenhao, et al.
Published: (2024)
Optimized Weight Initialization on the Stiefel Manifold for Deep ReLU Neural Networks
by: Lee, Hyungu, et al.
Published: (2025)
by: Lee, Hyungu, et al.
Published: (2025)
Weight Initialization and Variance Dynamics in Deep Neural Networks and Large Language Models
by: Han, Yankun
Published: (2025)
by: Han, Yankun
Published: (2025)
On the Effectiveness of Random Weights in Graph Neural Networks
by: Bui, Thu, et al.
Published: (2025)
by: Bui, Thu, et al.
Published: (2025)
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)
Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis
by: Lee, Hyunwoo, et al.
Published: (2024)
by: Lee, Hyunwoo, et al.
Published: (2024)
Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
by: Armeniakos, Giorgos, et al.
Published: (2024)
by: Armeniakos, Giorgos, 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)
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
by: Makrygiorgos, Georgios, et al.
Published: (2025)
by: Makrygiorgos, Georgios, et al.
Published: (2025)
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks
by: Bencomo, Gianluca, et al.
Published: (2025)
by: Bencomo, Gianluca, et al.
Published: (2025)
Prior-Informed Neural Network Initialization: A Spectral Approach for Function Parameterizing Architectures
by: Torres, David Orlando Salazar, et al.
Published: (2026)
by: Torres, David Orlando Salazar, et al.
Published: (2026)
LDLT L-Lipschitz Network Weight Parameterization Initialization
by: Juston, Marius F. R., et al.
Published: (2026)
by: Juston, Marius F. R., et al.
Published: (2026)
Similar Items
-
Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks
by: Kelesis, Dimitrios, et al.
Published: (2025) -
Partially Trained Graph Convolutional Networks Resist Oversmoothing
by: Kelesis, Dimitrios, et al.
Published: (2024) -
Tree-based Focused Web Crawling with Reinforcement Learning
by: Kontogiannis, Andreas, et al.
Published: (2021) -
Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
by: Park, MoonJeong, et al.
Published: (2024) -
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
by: Bougiatiotis, Konstantinos, et al.
Published: (2024)