Summary of A Review Of Graph Neural Networks in Epidemic Modeling, by Zewen Liu et al.
A Review of Graph Neural Networks in Epidemic Modeling
by Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau, Wei Jin
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph); Populations and Evolution (q-bio.PE)
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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 reviews Graph Neural Networks (GNNs) in epidemic tasks, highlighting potential future directions. It introduces hierarchical taxonomies for both epidemic tasks and methodologies, providing a development trajectory within this domain. The authors examine existing GNN-based methods, discussing limitations from diverse perspectives and proposing research directions. This survey aims to bridge literature gaps and promote the progression of the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at using special kinds of artificial intelligence called Graph Neural Networks (GNNs) to study how diseases spread. Right now, there are many different ways that scientists try to understand how diseases work, but GNNs can help make those models more accurate and useful. This paper is a big review of what’s been done so far with GNNs in this area, and it also suggests some new directions for research. |
Keywords
* Artificial intelligence * Gnn
