UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure

UniSAGE：利用超结构统一静态与动态属性

Abstract: 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.

摘要： 随着数字数据的快速增长，现实世界的应用越来越多地涉及结合了静态属性与动态记录的层级信息。以统一且可泛化的方式对这类异构数据进行建模仍然极具挑战性。现有的方法往往依赖大量的人工设计，与特定的数据模式紧密耦合，并且通常将静态和动态属性孤立处理，从而忽略了它们之间隐含的交互作用。

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.

我们提出了 UniSAGE，这是一个用于对同时包含静态和动态属性的数据进行建模的统一框架。UniSAGE 构建了一个全局属性图，以统一的结构表示层级关系和时间关系。为了确保表征的一致性，它引入了两个正交的参数子空间，在共享的语义空间内共同支持静态聚合和动态推理。

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.

基于这些统一的表征，UniSAGE 通过一种轻量级的超结构机制，进一步实现了静态属性与动态属性之间针对特定任务的交互。UniSAGE 是完全自动化的，对不断演变的数据模式具有鲁棒性，并能够捕捉复杂的跨属性依赖关系。在多个公共基准测试和一个真实金融行为数据集上的广泛实验表明，UniSAGE 的表现始终优于现有方法，在多个任务上实现了超过 10% 的性能提升。