Summary of Graph Learning Under Distribution Shifts: a Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning, by Man Wu et al.
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
by Man Wu, Xin Zheng, Qin Zhang, Xiao Shen, Xiong Luo, Xingquan Zhu, Shirui Pan
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 Graph learning plays a crucial role in various application scenarios, such as social network analysis and recommendation systems, by modeling complex data relations represented by graph structural data. However, real-world graph data often exhibit dynamics over time, leading to distribution shifts that significantly impact the performance of graph learning methods. This survey provides a comprehensive review of latest approaches, strategies, and insights addressing distribution shifts in graph learning. We categorize existing methods into scenarios like graph domain adaptation learning, out-of-distribution learning, and continual learning based on observability of distributions and availability of supervision information. For each scenario, we propose a detailed taxonomy with descriptions and discussions of progress made in distribution-shifted graph learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are a way to show connections between things. When these connections change over time, it’s hard for computers to keep up. That’s why researchers have been working on ways to make computers better at understanding changing graphs. This paper looks at all the different methods people have tried to solve this problem. It groups them into categories like “learning from a new type of data” or “continuously learning from changes in data”. The paper also talks about what these methods are good for and where they might be useful. |
Keywords
* Artificial intelligence * Continual learning * Domain adaptation
