Summary of Can Llms Convert Graphs to Text-attributed Graphs?, by Zehong Wang et al.
Can LLMs Convert Graphs to Text-Attributed Graphs?
by Zehong Wang, Sidney Liu, Zheyuan Zhang, Tianyi Ma, Chuxu Zhang, Yanfang Ye
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes a novel method called Topology-Aware Node description Synthesis (TANS) to convert existing graphs into text-attributed graphs, leveraging large language models (LLMs). This approach integrates topological information into LLMs to explain how graph topology influences node semantics. The TANS is evaluated on text-rich, text-limited, and text-free graphs, demonstrating its applicability. Specifically, the method significantly outperforms existing approaches that manually design node features on text-free graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses graph neural networks (GNNs) to model graph-structured data, but existing architectures have challenges in cross-graph learning where multiple graphs have different feature spaces. To address this, recent approaches introduce text-attributed graphs (TAGs), where each node is associated with a textual description that can be projected into a unified feature space using textual encoders. The TANS method bridges the gap between graph-structured data and textual information by converting existing graphs into TAGs. |
Keywords
» Artificial intelligence » Semantics
