Summary of Logical Distillation Of Graph Neural Networks, by Alexander Pluska et al.
Logical Distillation of Graph Neural Networks
by Alexander Pluska, Pascal Welke, Thomas Gärtner, Sagar Malhotra
First submitted to arxiv on: 11 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 A logic-based interpretable model for learning on graphs is proposed, along with an algorithm to distill this model from a Graph Neural Network (GNN). The model leverages an extension of the two-variable fragment of first-order logic with counting quantifiers (C2) to distill interpretable logical classifiers. The approach is tested on multiple GNN architectures, and the distilled models are found to be interpretable, succinct, and achieve similar accuracy to the underlying GNN. Furthermore, when the ground truth is expressible in C2, the approach outperforms the GNN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to learn about graphs using logic, which helps us understand how the model works. It takes a Graph Neural Network (GNN) and makes it simpler and easier to read, while keeping its accuracy. This is important because GNNs are hard to understand, but we can use this approach to make them more interpretable. |
Keywords
» Artificial intelligence » Gnn » Graph neural network
