Summary of Self-explainable Temporal Graph Networks Based on Graph Information Bottleneck, by Sangwoo Seo et al.
Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck
by Sangwoo Seo, Sungwon Kim, Jihyeong Jung, Yoonho Lee, Chanyoung Park
First submitted to arxiv on: 19 Jun 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 A novel framework, Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck (TGIB), is proposed for explaining predictions of Temporal Graph Neural Networks (TGNN). TGIB introduces stochasticity in each temporal event to provide explanations for event occurrences based on the Information Bottleneck theory. The model simultaneously performs prediction and explanation for temporal graphs, offering improved link prediction performance and explainability compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a way to understand how predictions are made by Temporal Graph Neural Networks (TGNNs). These networks can look at the relationships between things over time. But it’s hard to figure out why they make certain predictions. The researchers created a new model that explains these predictions while making them. This model is called Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck (TGIB). It works by adding some randomness to each event in the timeline, so we can see how it got there. |
