Summary of Graph Neural Networks For Tabular Data Learning: a Survey with Taxonomy and Directions, by Cheng-te Li et al.
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
by Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 provides a comprehensive review of Graph Neural Networks (GNNs) used in Tabular Data Learning (TDL), highlighting the underrepresentation of latent correlations among data instances and feature values. The paper presents a systematic investigation into GNN-based TDL methods, including foundational aspects, various training plans, and practical applications across different scenarios. Key findings include the potential for GNNs to enhance instance representations through auxiliary tasks and their versatility in revolutionizing TDL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are a type of deep learning that helps computers understand relationships between data points. In this paper, researchers explore how GNNs can be used to improve learning from tables of data. They find that GNNs are good at discovering hidden patterns in the data and that using them can lead to better results than traditional methods. |
Keywords
* Artificial intelligence * Deep learning * Gnn
