Summary of Learning on Multimodal Graphs: a Survey, by Ciyuan Peng et al.
Learning on Multimodal Graphs: A Survey
by Ciyuan Peng, Jiayuan He, Feng Xia
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 This survey paper explores the field of Multimodal Graph Learning (MGL), which is crucial for successful AI applications in domains such as healthcare, social media, and transportation. The paper conducts a comparative analysis of existing works in MGL, highlighting how multimodal learning is achieved across different graph types and exploring prevalent learning techniques. Additionally, it outlines significant applications of MGL and offers insights into future directions in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal Graph Learning (MGL) is a way to use artificial intelligence (AI) in areas like healthcare, social media, and transportation. This paper looks at what’s already been done in this field and how it works. It helps us understand how AI can be used with different types of graphs and data. |
