Summary of Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in Gnns: a Survey, by Hugo Attali et al.
Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey
by Hugo Attali, Davide Buscaldi, Nathalie Pernelle
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new survey paper examines the limitations of Graph Neural Networks (GNNs) in learning from graph-structured data, which are caused by oversquashing and oversmoothing. Oversquashing occurs when information from distant nodes is compressed excessively, resulting in significant loss, while oversmoothing happens when repeated message-passing iterations homogenize node representations, obscuring meaningful distinctions. To address these challenges, the paper focuses on graph rewiring techniques that modify graph topology to enhance information diffusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph Neural Networks (GNNs) are special tools that help us learn from data with connections between things. But sometimes, this kind of learning gets stuck because too much important information gets lost or mixed up. To fix this, researchers use a technique called graph rewiring, which changes the way these connected things are linked together. This helps new information flow in and old information get kept. |
Keywords
* Artificial intelligence * Diffusion
