Summary of Graphs Generalization Under Distribution Shifts, by Qin Tian et al.
Graphs Generalization under Distribution Shifts
by Qin Tian, Wenjun Wang, Chen Zhao, Minglai Shao, Wang Zhang, Dong Li
First submitted to arxiv on: 25 Mar 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 Traditional machine learning methods rely heavily on independent and identically distributed data, which can lead to limitations when the test distribution deviates from the training distribution. To address this issue, researchers have been exploring out-of-distribution (OOD) generalization, aiming to achieve satisfactory performance in unknown distribution shifts. However, OOD methods for graph-structured data remain unexplored due to two primary challenges: simultaneous node attribute and graph topology changes, and capturing invariant information amidst diverse distribution shifts. This paper introduces a novel framework, Graph Learning Invariant Domain genERation (GLIDER), which aims to diversify domain variations by modeling seen or unseen attribute and topological structure changes, while minimizing representation space discrepancies for predicting semantic labels. Experimental results show that GLIDER outperforms baseline methods in node-level OOD generalization across domains with simultaneous distribution shifts on node features and topological structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a machine to recognize pictures or understand text, but the pictures or text look very different from what it’s used to. This is called “out-of-distribution” learning, where the computer needs to adapt to new and unfamiliar data. Researchers have been working on this problem for graph-structured data, which means they’re trying to teach machines to recognize patterns in complex networks like social media or brain connections. The main challenge is that these networks can change in many ways at once, making it hard for machines to learn from them. To solve this problem, the researchers introduce a new framework called GLIDER, which helps machines adapt to new data by understanding how different pieces of information relate to each other. |
Keywords
* Artificial intelligence * Generalization * Machine learning
