ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2406.02269
  4. Cited By
Graph Neural Networks Do Not Always Oversmooth

Graph Neural Networks Do Not Always Oversmooth

4 June 2024
Bastian Epping
Alexandre René
M. Helias
Michael T. Schaub
ArXiv (abs)PDFHTML

Papers citing "Graph Neural Networks Do Not Always Oversmooth"

3 / 3 papers shown
Title
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
Michela Lapenna
Caterina De Bacco
277
1
0
13 Jun 2025
Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
Adrian Arnaiz-Rodriguez
Federico Errica
AI4CE
284
7
0
21 May 2025
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Alvaro Arroyo
Alessio Gravina
Benjamin Gutteridge
Federico Barbero
Claudio Gallicchio
Xiaowen Dong
Michael M. Bronstein
P. Vandergheynst
273
29
0
15 Feb 2025
1