Summary of Improving Graph Out-of-distribution Generalization on Real-world Data, by Can Xu et al.
Improving Graph Out-of-distribution Generalization on Real-world Data
by Can Xu, Yao Cheng, Jianxiang Yu, Haosen Wang, Jingsong Lv, Xiang Li
First submitted to arxiv on: 14 Jul 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 The proposed method, “Probability Dependency on Environments and Rationales for OOD Graphs on Real-world Data” (DEROG), addresses graph out-of-distribution (OOD) generalization by recognizing the importance of environment-label dependencies and mutable rationale invariance. This paper presents novel theorems that characterize the role of environments in determining graph labels, as well as the mutable importance of graph rationales. A variational inference-based approach is introduced to alleviate the impact of unknown prior knowledge on environments and rationales. The method employs generalized Bayesian inference and an EM-based algorithm for optimization. Experimental results demonstrate the superiority of DEROG on real-world datasets under different distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can learn from graphs that are new and unusual. Right now, most methods only work well when they’re shown lots of examples of similar graphs. But what if we wanted to use these methods in real-life situations where the graphs might be very different? That’s a problem! The authors of this paper have come up with some new ideas that can help us solve this problem. They’ve developed a special method called DEROG, which stands for “Probability Dependency on Environments and Rationales for OOD Graphs on Real-world Data”. This method helps computers learn from graphs even when they’re very different from what we’ve seen before. |
Keywords
» Artificial intelligence » Bayesian inference » Generalization » Inference » Optimization » Probability
