UniSAGE：用超结构统一静态和动态属性
UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure
- Zhejiang University（浙江大学）
- FinVolution Group（纷智科技集团）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对结合静态与动态属性的异构数据建模难题，提出UniSAGE框架，构建全局属性图，引入正交参数子空间，通过轻量级超结构机制实现特定任务交互，实验证明其性能优于现有方法，能捕获复杂依赖。
AI中文摘要：
随着数字数据的快速增长，现实世界应用越来越多地涉及将静态属性与动态记录相结合的层次信息。以统一且可推广的方式对这种异构数据建模仍具有挑战性。现有方法常依赖大量人工设计，与特定数据模式紧密耦合，且通常孤立处理静态和动态属性，忽略其隐含交互。我们提出UniSAGE，一个用于对具有静态和动态属性的数据进行建模的统一框架。UniSAGE构建一个在统一结构中表示层次和时间关系的全局属性图。为确保表示一致性，它引入两个正交参数子空间，在共享语义空间中共同支持静态聚合和动态推理。基于这些统一表示，UniSAGE通过轻量级超结构机制进一步实现静态和动态属性之间的特定任务交互。UniSAGE完全自动化，对不断演变的数据模式具有鲁棒性，能够捕获复杂的跨属性依赖关系。在多个公共基准和一个真实世界金融行为数据集上的大量实验表明，UniSAGE始终优于现有方法，在多个任务上实现了超过10%的性能提升。
英文摘要：
With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such heterogeneous data in a unified and generalizable manner remains challenging. Existing approaches often rely on extensive manual design, are tightly coupled to specific data schemas, and typically process static and dynamic attributes in isolation, thereby overlooking their implicit interactions. We propose UniSAGE, a unified framework for modeling data with both static and dynamic attributes. UniSAGE constructs a global attribute graph that represents hierarchical and temporal relationships in a unified structure. To ensure representational consistency, it introduces two orthogonal parameter subspaces that jointly support static aggregation and dynamic reasoning within a shared semantic space. Building on these unified representations, UniSAGE further enables task-specific interaction between static and dynamic attributes via a lightweight hyper-structure mechanism. UniSAGE is fully automated, robust to evolving data schemas, and capable of capturing complex cross-attribute dependencies. Extensive experiments on multiple public benchmarks and a real-world financial behavior dataset demonstrate that UniSAGE consistently outperforms existing methods, achieving performance improvements of over 10% on several tasks.
