Summary of Tagexplainer: Narrating Graph Explanations For Text-attributed Graph Learning Models, by Bo Pan et al.
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models
by Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang, Yifei Zhang, Liang Zhao
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 TAGExplainer, for generating natural language explanations for Text-Attributed Graphs (TAGs). TAGExplainer is designed to overcome the black-box nature of existing TAG representation learning models. The approach employs a generative language model that maps input-output pairs to explanations reflecting the model’s decision-making process. To address the lack of annotated ground truth explanations in real-world scenarios, the paper proposes generating pseudo-labels based on saliency-based explanations and iteratively training an explanation generator model. Extensive experiments demonstrate the effectiveness of TAGExplainer in producing faithful and concise natural language explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to explain why machine learning models make certain decisions when working with social networks or recommendation systems. The authors created a tool called TAGExplainer that can generate simple, easy-to-understand explanations for these models. This tool is important because it helps people understand how and why the models work, which can be useful in many different areas. |
Keywords
» Artificial intelligence » Language model » Machine learning » Representation learning
