Summary of The Intelligible and Effective Graph Neural Additive Networks, by Maya Bechler-speicher et al.
The Intelligible and Effective Graph Neural Additive Networks
by Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents Graph Neural Additive Network (GNAN), a novel interpretable model designed to provide transparent explanations in high-stakes scenarios. Unlike traditional black-box GNNs, GNAN can be visualized and fully understood by humans, offering both global and local explanations at the feature and graph levels. The model is an extension of Generalized Additive Models and can describe how it uses relationships between the target variable, features, and graphs. Empirically, GNAN demonstrates high accuracy comparable to black-box GNNs on various tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of computer program that helps us understand how machines make decisions about things connected in a graph. The program is special because it’s transparent – we can see exactly what the machine is doing when it makes predictions. This is important for situations where mistakes could have big consequences. The program works just as well as other programs, but we can trust it to be fair and explainable. |
