Summary of Gft: Graph Foundation Model with Transferable Tree Vocabulary, by Zehong Wang et al.
GFT: Graph Foundation Model with Transferable Tree Vocabulary
by Zehong Wang, Zheyuan Zhang, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 aims to address the lack of Graph Foundation Models (GFMs) that can achieve desired performance on various graph-learning-related tasks. Inspired by the success of foundation models in applications such as ChatGPT, the authors propose a cross-task, cross-domain graph foundation model named GFT, which improves model generalization and reduces the risk of negative transfer by treating computation trees as tokens within a transferable vocabulary. Theoretical analyses and extensive experimental studies demonstrate the transferability of computation trees and show the effectiveness of GFT across diverse tasks and domains in graph learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how different things are connected, like people on social media or molecules in chemistry. Graph Foundation Models (GFMs) can help with this by finding patterns in these connections. But right now, we don’t have good models that work well for many types of graphs. The authors of this paper think about how to make better GFMs by looking at the way information flows through these graphs. They create a new model called GFT that works better than other models and can be used in different areas like science, social media, and medicine. |
Keywords
» Artificial intelligence » Generalization » Transferability
