Summary of Graph Domain Adaptation: Challenges, Progress and Prospects, by Boshen Shi et al.
Graph Domain Adaptation: Challenges, Progress and Prospects
by Boshen Shi, Yongqing Wang, Fangda Guo, Bingbing Xu, Huawei Shen, Xueqi Cheng
First submitted to arxiv on: 1 Feb 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 This abstract presents a comprehensive overview of graph domain adaptation (GDA), a knowledge-transfer paradigm that adapts the knowledge learnt from source graphs to target graphs. GDA combines the advantages of graph representation learning and domain adaptation, making it a promising direction for transfer learning on graphs. The paper reviews recent advances in GDA, outlining research status and challenges, proposing a taxonomy, introducing representative works, and discussing prospects. This survey aims to provide a thorough understanding of GDA, highlighting its applications and potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GDA is a way to teach machines how to recognize patterns on different kinds of graphs. Think of graphs like social networks or transportation systems – they’re all about connections between things. When we have limited information (labels) about these graphs, GDA helps us learn from similar graphs with more information. This makes it easier for machines to recognize patterns and make predictions on the target graph. The paper reviews recent studies and advancements in GDA, showing how it’s become an important area of research. |
Keywords
* Artificial intelligence * Domain adaptation * Representation learning * Transfer learning
