Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs

Creative Commons License

Baş S., Kaya Ünal K., Tugay R., Öğüdücü Ş.

24th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, Vilniaus, Litvanya, 9 - 13 Eylül 2024, cilt.2560 CCIS, ss.347-358, (Tam Metin Bildiri) identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2560 CCIS
  • Doi Numarası: 10.1007/978-3-032-25311-8_27
  • Basıldığı Şehir: Vilniaus
  • Basıldığı Ülke: Litvanya
  • Sayfa Sayıları: ss.347-358
  • Anahtar Kelimeler: Generative models, Graph neural networks, Graph sequentialization, Data augmentation
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Gazi Üniversitesi Adresli: Evet

Özet

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.