统一建模序列与关系数据，提升预测任务表现
Bridging the Divide: End-to-End Sequence-Graph Learning
- 设计端到端架构，联合学习序列与图结构信息
- 在关系预测和欺诈检测中超越现有方法
- 适合处理客户、患者等具时序与关系的数据
许多现实世界的预测任务，尤其是涉及客户或患者等实体的任务，同时包含序列数据和关系数据。每个实体既有自身的事件序列，又与其他实体存在关联。现有的序列与图模型方法通常忽略其中一种模态。我们提出将两种特性整合并联合学习。引入BRIDGE，一种统一的端到端架构，在单一目标下耦合序列模型与图模块，使梯度可在两者间流动，学习任务对齐的表示。为实现细粒度交互，提出TOKENXATTN，一种基于令牌的跨注意力层，促进相邻序列中特定事件之间的消息传递。在关系预测与欺诈检测两个场景中，BRIDGE在排序与分类指标上均持续优于静态图模型、时序图方法及仅序列基线。
原文摘要 · Abstract (English)
Many real-world prediction tasks, particularly those involving entities such as customers or patients, involve both {sequential} and {relational} data. Each entity maintains its own sequence of events while simultaneously engaging in relationships with others. Existing methods in sequence and graph modeling often overlook one modality in favor of the other. We argue that these two facets should instead be integrated and learned jointly. We introduce BRIDGE, a unified end-to-end architecture that couples a sequence model with a graph module under a single objective, allowing gradients to flow across both components to learn task-aligned representations. To enable fine-grained interaction, we propose TOKENXATTN, a token-level cross-attention layer that facilitates message passing between specific events in neighboring sequences. Across two settings, relationship prediction and fraud detection, BRIDGE consistently outperforms static graph models, temporal graph methods, as well as sequence-only baselines on both ranking and classification metrics.
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