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

A GAN Approach for Node Embedding in Heterogeneous Graphs Using Subgraph Sampling