Relational multi-table data is common in domains such as e-commerce, healthcare, and scientific research, and can be naturally represented as heterogeneous temporal graphs with multi-modal node attributes. Existing graph neural networks (GNNs) rely on schema-specific feature encoders, requiring separate modules for each node type and feature column, which hinders scalability and parameter sharing. We introduce RELATE (Relational Encoder for Latent Aggregation of Typed Entities), a schema-agnostic, plug-and-play feature encoder that can be used with any general purpose GNN. RELATE employs shared modality-specific encoders for categorical, numerical, textual, and temporal attributes, followed by a Perceiver-style cross-attention module that aggregates features into a fixed-size, permutation-invariant node representation. We evaluate RELATE on ReLGNN and HGT in the RelBench benchmark, where it achieves performance within 3% of schema-specific encoders while reducing parameter counts by up to 5x. This design supports varying schemas and enables multi-dataset pretraining for general-purpose GNNs, paving the way toward foundation models for relational graph data.

翻译：关系型多表数据在电子商务、医疗健康和科学研究等领域中普遍存在，可自然地表示为具有多模态节点属性的异质时序图。现有的图神经网络（GNNs）依赖于特定于模式的特征编码器，需要为每种节点类型和特征列设计独立模块，这限制了模型的可扩展性和参数共享能力。本文提出RELATE（面向类型化实体潜在聚合的关系编码器），一种无模式、即插即用的特征编码器，可与任何通用GNN配合使用。RELATE采用共享的模态专用编码器处理分类、数值、文本和时序属性，随后通过感知器风格的交叉注意力模块将特征聚合为固定尺寸、排列不变的节点表示。我们在RelBench基准测试中使用ReLGNN和HGT评估RELATE，其性能达到特定模式编码器的97%以上，同时将参数量减少至最高五分之一。该设计支持可变模式，并为通用GNN实现多数据集预训练，为构建关系图数据的基础模型开辟了道路。