  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.
