Summary of A Survey Of Out-of-distribution Generalization For Graph Machine Learning From a Causal View, by Jing Ma
A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View
by Jing Ma
First submitted to arxiv on: 15 Sep 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 reviews recent advancements in using causality-driven approaches to improve the generalization capabilities of Graph Machine Learning (GML) across different environments. Unlike traditional GML methods, which rely on statistical dependencies, causality-focused strategies explore underlying causal mechanisms of data generation and model prediction. This review categorizes various approaches, providing detailed descriptions of their methodologies and connections among them. The incorporation of causality in trustworthy GML is explored, including explanation, fairness, and robustness. The paper highlights the potential future research directions for enhancing the trustworthiness of graph machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make Graph Machine Learning (GML) work better when it’s faced with new data that’s different from what it was trained on. Right now, GML is good at doing certain tasks, but it struggles when it sees data that’s never seen before. The authors of this review talk about a new way to approach GML, using something called causality to help it generalize better. They explain the ideas behind these approaches and how they can be used in other important areas like making sure AI is fair and transparent. |
Keywords
» Artificial intelligence » Generalization » Machine learning
