One main challenge in imbalanced graph classification is to learn expressive
representations of the graphs in under-represented (minority) classes. Existing
generic imbalanced learning methods, such as oversampling and imbalanced
learning loss functions, can be adopted for enabling graph representation
learning models to cope with this challenge. However, these methods often
directly operate on the graph representations, ignoring rich discriminative
information within the graphs and their interactions. To tackle this issue, we
introduce a novel multi-scale oversampling graph neural network (MOSGNN) that
learns expressive minority graph representations based on intra- and
inter-graph semantics resulting from oversampled graphs at multiple scales -
subgraph, graph, and pairwise graphs. It achieves this by jointly optimizing
subgraph-level, graph-level, and pairwise-graph learning tasks to learn the
discriminative information embedded within and between the minority graphs.
Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i)
significantly outperforms five state-of-the-art models, and ii) offers a
generic framework, in which different advanced imbalanced learning loss
functions can be easily plugged in and obtain significantly improved
classification performance.

一个主要的挑战是在不平衡的图分类中学习表达图的表达性，并通过在多个尺度 - 子图、图和成对图上超采样得到的图内和图间语义学习达到这一目标。MOSGNN 通过联合优化子图级、图级和成对图级学习任务，学习镶嵌在少数类图中和图之间的鉴别性信息，从而显著优于五种最先进的模型，并提供一个通用框架，在其中可以轻松插入不同的先进的不对称学习损失函数，并获得明显的改进分类性能。