Summary of Unleashing the Potential Of Text-attributed Graphs: Automatic Relation Decomposition Via Large Language Models, by Hyunjin Seo et al.
Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models
by Hyunjin Seo, Taewon Kim, June Yong Yang, Eunho Yang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The abstract discusses a novel approach to enhancing Graph Neural Networks (GNNs) by decomposing edges in text-attributed graphs into distinct semantic relations. Previous work has focused on using language models to improve node features, but this study reveals that conventional edges actually encompass mixed semantics, hindering representation learning. The authors introduce RoSE, a framework that uses Large Language Models (LLMs) to automate edge decomposition and categorization. This framework operates in two stages: identifying meaningful relations and analyzing textual contents associated with connected nodes. Experimental results show significant improvements in node classification performance across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can improve Graph Neural Networks by looking deeper into the edges of text-attributed graphs. Right now, these edges are treated as just one type of connection, but this study shows that they actually have different meanings. This makes it harder for GNNs to learn and make good predictions. To fix this, the authors create a new way called RoSE that uses special language models to automatically break down the edges into their different meanings. This helps GNNs work better and can improve performance by up to 16%. |
Keywords
» Artificial intelligence » Classification » Representation learning » Semantics
