Summary of Gintrip: Interpretable Temporal Graph Regression Using Information Bottleneck and Prototype-based Method, by Ali Royat et al.
GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
by Ali Royat, Seyed Mohamad Moghadas, Lesley De Cruz, Adrian Munteanu
First submitted to arxiv on: 17 Sep 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 The proposed GINTRIP framework integrates Information Bottleneck (IB) principles with prototype-based methods to enhance the interpretability of temporal graph regression models. The approach introduces a novel bound on mutual information (MI), extending IB’s applicability to graph regression tasks. By incorporating an unsupervised auxiliary classification head, the model fosters multi-task learning and diverse concept representation, improving its bottleneck’s interpretability. The GINTRIP framework is evaluated on real-world traffic datasets, outperforming existing methods in both forecasting accuracy and interpretability-related metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach to making graph neural networks (GNNs) more understandable uses a combination of techniques from two areas: information bottlenecks and prototypes. This helps make GNNs better at predicting future events on complex networks, like traffic patterns. The method is tested on real-world data and performs well in both prediction accuracy and how easily its results can be understood. |
Keywords
» Artificial intelligence » Classification » Multi task » Regression » Unsupervised
