Summary of Position: Graph Foundation Models Are Already Here, by Haitao Mao et al.
Position: Graph Foundation Models are Already Here
by Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang
First submitted to arxiv on: 3 Feb 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 presents a novel approach to developing Graph Foundation Models (GFMs) that overcome the challenges of traditional Graph Neural Networks (GNNs). GFMs are trained on diverse graph data, enabling positive transfer across tasks and domains. The primary challenge lies in effectively leveraging this vast data. Inspired by foundation models in computer vision and natural language processing, the authors propose a “graph vocabulary” perspective, which grounds graph encoding in network analysis, expressiveness, and stability. This approach has the potential to advance GFM design according to neural scaling laws. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating special kinds of AI models that work with graphs. Graphs are like networks or maps, and these models can learn from lots of different types of graph data. The challenge is making sure they can use this learning to do well on new tasks and in different areas. The authors have a new idea for how to make these models better by looking at what makes graphs similar or different. This could help us create even more powerful AI models that can work with lots of different types of data. |
Keywords
* Artificial intelligence * Natural language processing * Scaling laws
