Summary of Self-explainable Graph Transformer For Link Sign Prediction, by Lu Li et al.
Self-Explainable Graph Transformer for Link Sign Prediction
by Lu Li, Jiale Liu, Xingyu Ji, Maojun Wang, Zeyu Zhang
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Medium Difficulty summary: The paper introduces a new framework called Self-Explainable Signed Graph transformer (SE-SGformer) to improve the explainability of signed graph neural networks (SGNNs). SGNNs are effective in analyzing complex patterns in real-world situations where positive and negative links coexist, but they lack transparency. SE-SGformer combines a Transformer architecture for signed graphs with positional encoding based on signed random walks, which has greater expressive power than existing methods. The framework also includes an explainable decision process that discovers the K-nearest positive or negative neighbors of a node to predict edge signs. This approach improves prediction accuracy by 2.2% and explainability accuracy by 73.1% compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers are working on a new type of artificial intelligence called signed graph neural networks that can analyze complex patterns in real-world situations. However, these models don’t provide clear explanations for their predictions. The goal of this paper is to make these models more transparent and understandable. To achieve this, the authors introduce a new framework called Self-Explainable Signed Graph transformer (SE-SGformer) that can predict edge signs while providing important information about why certain edges are positive or negative. This approach improves the accuracy of predictions by 2.2% and makes them more explainable by 73.1%. |
Keywords
» Artificial intelligence » Positional encoding » Transformer
