Summary of Do Neural Scaling Laws Exist on Graph Self-supervised Learning?, by Qian Ma et al.
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?
by Qian Ma, Haitao Mao, Jingzhe Liu, Zhehua Zhang, Chunlin Feng, Yu Song, Yihan Shao, Yao Ma
First submitted to arxiv on: 20 Aug 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 This paper investigates the feasibility of using existing self-supervised learning (SSL) techniques in graph domains to develop Graph Foundation Models (GFMs). The neural scaling law, where performance improves with increasing model and dataset sizes, has been observed in natural language processing (NLP) and computer vision (CV) domains. However, it is unclear if this holds true for graph SSL techniques. The authors examine various existing graph SSL implementations on comprehensive benchmarks, including conventional and new settings adopted from other domains. Surprisingly, they find that none of the tested graph SSL techniques follow the neural scaling behavior in terms of downstream performance. Instead, model architecture and pretext task design have a more significant impact on performance. This study highlights the importance of designing graph SSL techniques specifically for GFMs and opens up a new direction for research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to train artificial intelligence models to learn from huge amounts of data without human supervision. This is called self-supervised learning, or SSL. Researchers have found that this technique works well in certain areas like language and image recognition. But what about graph-based data, which is used for things like social network analysis? The authors of this paper investigated whether existing SSL techniques can be used to develop these graph-based models, known as Graph Foundation Models (GFMs). They tested various methods on large datasets and found that none of them followed a pattern where performance improves with increasing model and dataset sizes. Instead, the choice of model architecture and task design had more influence. This study shows that developing new SSL techniques specifically for graph-based data is important. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Self supervised
