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.

翻译：我们的研究使用图神经网络（GNNs）解决异构图中的类别不平衡问题。我们提出了一种新颖方法，结合生成对抗网络（GANs）与GNNs的优势，合成节点和边以有效平衡数据集。该方法直接在数据层面针对并纠正不平衡现象。所提框架解决了数据生成过程中忽略图结构的问题，并生成了可用于下游任务中基于GNN分类器的合成结构。它同时处理节点和边信息，通过节点增强和子图采样改善边的平衡性。此外，该框架集成了阈值策略，有助于在训练过程中确定最优边阈值，而无需进行耗时的参数调整。在Amazon和Yelp Review数据集上的实验突显了所提框架的有效性，特别是在少数节点识别方面，其在关键性能指标上始终优于基线模型，展现了该领域的应用潜力。