Summary of Data Augmentation in Graph Neural Networks: the Role Of Generated Synthetic Graphs, by Sumeyye Bas et al.
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs
by Sumeyye Bas, Kiymet Kaya, Resul Tugay, Sule Gunduz Oguducu
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Theory (cs.IT)
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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 Graph Neural Networks (GNNs) are enhanced by capturing complex relationships between interrelated data, represented as graphs. To achieve high-quality graph representation, identifying linked patterns is crucial. However, challenges like data scarcity, collection costs, and ethical concerns hinder progress. Generative models and data augmentation have become popular alternatives. This study explores using generated graphs for data augmentation, comparing performance combining generated and real graphs, and examining the effect of varying quantities on graph classification tasks. The results show that balancing scalability and quality requires different generators based on graph size. A new approach to graph data augmentation is introduced, ensuring consistent labels and enhancing classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are important for understanding relationships in data. To make predictions better, we need to improve how we represent these relationships. One way to do this is by using computers to generate fake graphs that help us train our models. This study looks at how well this works, especially when combining real and fake graphs. The results show that different generators are needed for different-sized graphs. A new method for generating fake graphs was developed, which helps with training models. |
Keywords
* Artificial intelligence * Classification * Data augmentation
