  Our research addresses class imbalance issues in heterogeneous graphs using
graph neural networks (GNNs). We propose a novel method combining the strengths
of Generative Adversarial Networks (GANs) with GNNs, creating synthetic nodes
and edges that effectively balance the dataset. This approach directly targets
and rectifies imbalances at the data level. The proposed framework resolves
issues such as neglecting graph structures during data generation and creating
synthetic structures usable with GNN-based classifiers in downstream tasks. It
processes node and edge information concurrently, improving edge balance
through node augmentation and subgraph sampling. Additionally, our framework
integrates a threshold strategy, aiding in determining optimal edge thresholds
during training without time-consuming parameter adjustments. Experiments on
the Amazon and Yelp Review datasets highlight the effectiveness of the
framework we proposed, especially in minority node identification, where it
consistently outperforms baseline models across key performance metrics,
demonstrating its potential in the field.
