In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneous entity graph and adopt the graph neural network (GNN) as the predictive model. However, existing RDL methods neglect the imbalance problem of relational data in RDBs and risk under-representing the minority entities, leading to an unusable model in practice. In this work, we investigate, for the first time, class imbalance problem in RDB entity classification and design the relation-centric minority synthetic over-sampling GNN (Rel-MOSS), in order to fill a critical void in the current literature. Specifically, to mitigate the issue of minority-related information being submerged by majority counterparts, we design the relation-wise gating controller to modulate neighborhood messages from each individual relation type. Based on the relational-gated representations, we further propose the relation-guided minority synthesizer for over-sampling, which integrates the entity relational signatures to maintain relational consistency. Extensive experiments on 12 entity classification datasets provide compelling evidence for the superiority of Rel-MOSS, yielding an average improvement of up to 2.46% and 4.00% in terms of Balanced Accuracy and G-Mean, compared with SOTA RDL methods and classic methods for handling class imbalance.

翻译：近期研究为在关系数据库（RDB）上实现完全数据驱动的学习范式，提出关系深度学习（RDL）方法，将RDB结构化为异质实体图，并采用图神经网络（GNN）作为预测模型。然而现有RDL方法忽略了RDB中关系数据的非平衡问题，存在少数类实体表征不足的风险，导致模型实际应用中失效。本文首次系统研究RDB实体分类中的类别非平衡问题，并提出面向关系中心的少数类合成过采样GNN模型（Rel-MOSS），以填补当前文献的关键空白。具体而言，为缓解少数类相关信息被多数类信息淹没的问题，我们设计了关系维度的门控控制器，对来自各关系类型的邻域消息进行调节。基于关系门控表征，我们进一步提出关系引导的少数类合成过采样方法，通过整合实体关系签名保持关系一致性。在12个实体分类数据集上的大量实验验证了Rel-MOSS的优越性，相比处理类别非平衡的最先进RDL方法及经典方法，其在均衡准确率和G-Mean指标上分别实现了平均2.46%和4.00%的提升。