Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929428980826112 |
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| author | Bas, Sumeyye Kaya, Kiymet Tugay, Resul Oguducu, Sule Gunduz |
| author_facet | Bas, Sumeyye Kaya, Kiymet Tugay, Resul Oguducu, Sule Gunduz |
| contents | Graphs are crucial for representing interrelated data and aiding predictive modeling by capturing complex relationships. Achieving high-quality graph representation is important for identifying linked patterns, leading to improvements in Graph Neural Networks (GNNs) to better capture data structures. However, challenges such as data scarcity, high collection costs, and ethical concerns limit progress. As a result, generative models and data augmentation have become more and more popular. This study explores using generated graphs for data augmentation, comparing the performance of combining generated graphs with real graphs, and examining the effect of different quantities of generated graphs on graph classification tasks. The experiments show that balancing scalability and quality requires different generators based on graph size. Our results introduce a new approach to graph data augmentation, ensuring consistent labels and enhancing classification performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14765 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs Bas, Sumeyye Kaya, Kiymet Tugay, Resul Oguducu, Sule Gunduz Machine Learning Artificial Intelligence Databases Information Theory Graphs are crucial for representing interrelated data and aiding predictive modeling by capturing complex relationships. Achieving high-quality graph representation is important for identifying linked patterns, leading to improvements in Graph Neural Networks (GNNs) to better capture data structures. However, challenges such as data scarcity, high collection costs, and ethical concerns limit progress. As a result, generative models and data augmentation have become more and more popular. This study explores using generated graphs for data augmentation, comparing the performance of combining generated graphs with real graphs, and examining the effect of different quantities of generated graphs on graph classification tasks. The experiments show that balancing scalability and quality requires different generators based on graph size. Our results introduce a new approach to graph data augmentation, ensuring consistent labels and enhancing classification performance. |
| title | Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs |
| topic | Machine Learning Artificial Intelligence Databases Information Theory |
| url | https://arxiv.org/abs/2407.14765 |