Summary of A Survey on Hypergraph Neural Networks: An In-depth and Step-by-step Guide, by Sunwoo Kim et al.
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
by Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a comprehensive survey on hypergraph neural networks (HNNs), which are a powerful tool for representation learning on hypergraphs. The authors break down existing HNN architectures into four design components: input features, input structures, message-passing schemes, and training strategies. They then examine how each of these components addresses and learns higher-order interactions (HOIs) in various applications such as recommendation systems, bioinformatics, medical science, time series analysis, and computer vision. The survey provides a step-by-step guide to HNNs and their potential uses in complex systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can learn from big data that is connected in many ways. It’s like trying to understand what’s going on in a big web of relationships. To do this, scientists have developed special kinds of computer models called hypergraph neural networks (HNNs). These models are very good at finding patterns and making predictions when there are lots of connections between things. The paper explains how these models work and shows some examples of how they can be used to make recommendations, understand biological data, analyze time series data, and even improve computer vision. |
Keywords
* Artificial intelligence * Representation learning * Time series
