Summary of Bridging Local Details and Global Context in Text-attributed Graphs, by Yaoke Wang et al.
Bridging Local Details and Global Context in Text-Attributed Graphs
by Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Yunfei Li, Siliang Tang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 proposes GraphBridge, a multi-granularity integration framework for representation learning on text-attributed graphs (TAGs). The framework bridges local and global perspectives by leveraging contextual textual information among nodes. This approach enhances fine-grained understanding of TAGs. Additionally, the authors introduce a graph-aware token reduction module to tackle scalability and efficiency challenges. Experimental results across various models and datasets demonstrate state-of-the-art performance and improved efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning from special kinds of graphs that have words attached to them. These graphs are important because they combine information from text and the structure of the graph. Most papers in this area focus on combining different types of information, but forget to look at how the words relate to each other. This new framework called GraphBridge looks at these word relationships to help understand the graph better. It also has a special tool to make it run faster and use less memory. |
Keywords
» Artificial intelligence » Representation learning » Token
