Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world datasets. Despite their success, most of current GNN models largely overlook the important setting of imbalanced class distribution, which typically results in prediction bias towards majority classes. To alleviate the prediction bias, we propose to leverage semantic structure of dataset based on the distribution of node embedding. Specifically, we present GraphDIVE, a general framework leveraging mixture of diverse experts (i.e., graph classifiers) for imbalanced graph classification. With a divide-and-conquer principle, GraphDIVE employs a gating network to partition an imbalanced graph dataset into several subsets. Then each expert network is trained based on its corresponding subset. Experiments on real-world imbalanced graph datasets demonstrate the effectiveness of GraphDIVE.

翻译：图表分类是一系列广泛领域许多应用中一个具有挑战性的研究问题。 在这些应用中,分类分布不平衡是十分常见的。 最近,图表神经网络(GNN)模型在各种真实世界数据集上取得了优异的性能。尽管取得了成功,但目前大多数GNN模型在很大程度上忽略了分类分布不平衡的重要背景,这通常导致对多数类的预测偏差。为了减轻预测偏差,我们提议利用基于节点嵌入分布的数据集的语义结构。具体地说,我们介绍“图形DIVE”,这是一个利用不同专家(如图表分类师)混合的通用框架,用于不平衡的图形分类。根据分化原则,“图形DimaDive”利用一个格子网络将不平衡的图表数据集分成几个子。然后,每个专家网络都根据其相应的子群接受培训。对真实世界不平衡的图表数据集进行了实验,显示了图形化的效果。