Summary of Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts, by Shiye Su et al.
Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts
by Shiye Su, Iulia Duta, Lucie Charlotte Magister, Pietro Liò
First submitted to arxiv on: 10 Oct 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 recent paper introduces SHypX, the first model-agnostic post-hoc explainer for hypergraph neural networks. These powerful models learn over hypergraphs, a generalization of graphs that describes relational data with higher-order interactions. However, their lack of interpretability has received limited attention until now. SHypX provides both local and global explanations using input attribution and unsupervised concept extraction methods. At the instance-level, it samples explanation subhypergraphs optimized for faithfulness and concision. At the model-level, it produces global explanation subhypergraphs that target a user-specified balance between these two factors. The paper demonstrates the effectiveness of SHypX across four real-world and four synthetic hypergraph datasets, showing improvements over baselines by 25 percent points in fidelity on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new paper explains how to make powerful models called hypergraph neural networks more understandable. These models are good at learning from complex data with many relationships between things. But it’s hard to figure out why they make certain predictions or decisions. The authors of this paper introduce a way to explain these models, which is important because we need to understand what our computers are doing. Their method, called SHypX, works by looking at individual pieces of data and the model’s overall behavior. It can also adjust how much detail it provides based on what you want to know. The authors tested their approach with many different datasets and showed that it’s better than other methods. |
Keywords
» Artificial intelligence » Attention » Generalization » Unsupervised
