Summary of Graphclip: Enhancing Transferability in Graph Foundation Models For Text-attributed Graphs, by Yun Zhu et al.
GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
by Yun Zhu, Haizhou Shi, Xiaotang Wang, Yongchao Liu, Yaoke Wang, Boci Peng, Chuntao Hong, Siliang Tang
First submitted to arxiv on: 14 Oct 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 The proposed GraphCLIP framework addresses the challenges of Text-Attributed Graphs (TAGs) by developing graph foundation models with strong cross-domain zero/few-shot transferability. The framework uses a self-supervised contrastive graph-summary pretraining method, leveraging Large Language Models (LLMs) to generate and curate large-scale graph-summary pair data. Additionally, the authors introduce a novel graph prompt tuning technique for few-shot learning, mitigating catastrophic forgetting and minimizing learning costs. Experimental results demonstrate the superiority of GraphCLIP in both zero-shot and few-shot settings, as well as its versatility across various downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs with text attributes are important, but current methods rely too much on labeled data and don’t work well across different situations. To fix this, researchers created a new way to train graph models that can be used in many different scenarios without needing labels. They also developed a technique to fine-tune these models when only a few examples are available. The new approach was tested and showed better results than previous methods. |
Keywords
» Artificial intelligence » Few shot » Pretraining » Prompt » Self supervised » Transferability » Zero shot
