Summary of Global Concept Explanations For Graphs by Contrastive Learning, By Jonas Teufel et al.
Global Concept Explanations for Graphs by Contrastive Learning
by Jonas Teufel, Pascal Friederich
First submitted to arxiv on: 25 Apr 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 proposed method extracts global concept explanations from the predictions of graph neural networks to develop a deeper understanding of tasks underlying structure-property relationships. This is achieved by identifying dense clusters in the self-explaining Megan models subgraph latent space, optimizing representative prototype graphs, and optionally providing hypotheses using GPT-4. The method is tested on synthetic and real-world graph property prediction tasks, demonstrating correct reproduction of structural rules for synthetic tasks and rediscovery of established rules for molecular property regression and classification tasks. Notably, the results for molecular mutagenicity prediction show fine-grained resolution of structural details surpassing existing explainability methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand complex graph property prediction tasks. It takes the predictions made by special types of artificial intelligence models called graph neural networks and turns them into explanations that can be understood by humans. These explanations are like clusters or groups in a special kind of space where the model’s ideas are stored. The method is tested on both made-up and real-world data and shows promise in understanding how certain structures affect the predictions. |
Keywords
» Artificial intelligence » Classification » Gpt » Latent space » Regression
