Summary of Graph Neural Networks For Brain Graph Learning: a Survey, by Xuexiong Luo et al.
Graph Neural Networks for Brain Graph Learning: A Survey
by Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David McAlpine, Paul Sowman, Alexis Giral, Philip S. Yu
First submitted to arxiv on: 1 Jun 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 paper reviews the current research methods in brain graph learning that utilize Graph Neural Networks (GNNs) for brain disorder analysis. The authors introduce a novel approach to modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and functional relationships among these regions as edges. They then systematically categorize existing works based on the type of brain graph generated and targeted research problems. The paper also provides an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, it presents future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to use computers to analyze the human brain and diagnose brain disorders. It reviews different ways that scientists are using special types of computer models called Graph Neural Networks (GNNs) to learn more about the brain. The authors show how GNNs can be used to model the brain as a complex network, with different parts of the brain connected by lines representing the relationships between them. They also summarize current research and provide suggestions for future studies. |
