Summary of Towards Graph Foundation Models: a Study on the Generalization Of Positional and Structural Encodings, by Billy Joe Franks et al.
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
by Billy Joe Franks, Moshe Eliasof, Semih Cantürk, Guy Wolf, Carola-Bibiane Schönlieb, Sophie Fellenz, Marius Kloft
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper explores the use of positional and structural encodings (PSEs) in graph neural networks (GNNs), which have shown improved performance across various tasks. It investigates the fine-tuning efficiency, scalability, and generalization capabilities of learnable PSEs on diverse graph datasets. The study evaluates PSEs as universal pre-trained models that can be adapted to new tasks with minimal fine-tuning and limited data. Additionally, it assesses the expressivity of learned representations when used to augment downstream GNNs. The findings demonstrate that PSEs generally enhance downstream models, but some datasets may require specific PSE-augmentations for optimal performance. This research contributes to the discussion on foundation models in graph learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how using positional and structural encodings (PSEs) in graph neural networks (GNNs) can help improve their performance. It tries to figure out if PSEs are a good way to make GNNs work well on many different kinds of graphs, and if they can be used as a starting point for other tasks with just a little extra training. The study also looks at how much information the learned representations can hold, and whether using them to help other GNNs can improve their performance. The results show that PSEs generally make downstream models work better, but some graphs might need special treatment to get the best results. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
