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.

翻译：非平衡图分类的主要挑战之一在于学习少数类图中富有表现力的表征。现有通用非平衡学习方法，如过采样和非平衡学习损失函数，可被用于赋能图表示学习模型以应对这一挑战。然而，这些方法通常直接作用于图表示，忽略了图内及图间丰富的判别性信息。为解决该问题，我们提出了一种新颖的多尺度过采样图神经网络(MOSGNN)，该网络基于在子图、图和对偶图三个尺度上生成的过采样样本所蕴含的图内及图间语义，学习富有表现力的少数类图表示。通过联合优化子图级、图级和对偶图级学习任务，MOSGNN能够挖掘少数类图内部及之间的判别性信息。在16个非平衡图数据集上的大量实验表明，MOSGNN(i)显著优于五种最先进模型，且(ii)提供了一个通用框架，可将不同的先进非平衡学习损失函数轻松嵌入其中，从而显著提升分类性能。