Summary of Towards Data-centric Machine Learning on Directed Graphs: a Survey, by Henan Sun et al.
Towards Data-centric Machine Learning on Directed Graphs: a Survey
by Henan Sun, Xunkai Li, Daohan Su, Junyi Han, Rong-Hua Li, Guoren Wang
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); 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 The paper presents a comprehensive review of directed graph learning from a data-centric perspective. It starts by introducing a novel taxonomy for existing studies on directed Graph Neural Networks (GNNs). The authors then re-examine these methods, emphasizing the importance of understanding and improving data representation in directed graphs. They demonstrate that model performance is heavily influenced by the quality of directed graphs. Additionally, they highlight the diverse applications of directed GNNs across 10+ domains, showcasing their broad applicability. The paper concludes by identifying key opportunities and challenges within the field, offering insights to guide future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews directed graph learning from a data-centric perspective. It shows that most Graph Neural Networks (GNNs) are limited because they simplify graphs into undirected formats, which loses important information. Directed graphs are better for modeling causal relationships and can be used in many areas like medicine, social networks, and more. The authors look at many different methods for learning directed graphs and show how understanding the quality of these graphs is key to making good models. They also explore many real-world applications and identify what needs to happen next in this field. |
