Summary of Machine Learning on Dynamic Graphs: a Survey on Applications, by Sanaz Hasanzadeh Fard
Machine Learning on Dynamic Graphs: A Survey on Applications
by Sanaz Hasanzadeh Fard
First submitted to arxiv on: 16 Jan 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 Dynamic graph learning has revolutionized the way we model complex interactions between entities across various real-world and scientific domains. Building upon the power of traditional graph representations, dynamic graph learning offers a powerful means to capture intricate dynamics among entities in transportation, brain, social, and internet networks. Furthermore, rapid advancements in machine learning have expanded the scope of dynamic graph applications beyond these domains, opening up new avenues for addressing diverse challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dynamic graph learning helps us understand complex interactions between different things, like people or places. This is useful for many areas, such as transportation, brain science, social media, and the internet. By using machine learning on dynamic graphs, we can solve problems that are hard to tackle with traditional methods. |
Keywords
* Artificial intelligence * Machine learning
