Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in this field mainly focus on a single fixed scale, either investigating individual trajectories at the micro-level or crowd flows among market entities at the macro-level. Consequently, the intrinsic cross-scale interactions between talents and the labor market are largely overlooked. To bridge this gap, we propose UniTRep, a novel unified representation learning framework for cross-scale career mobility analysis. Specifically, we first introduce a trajectory hypergraph structure to organize the career mobility patterns in a low-information-loss manner, where market entities and talent trajectories are represented as nodes and hyperedges, respectively. Then, for learning the market-aware talent representations, we attentively propagate the node information to the hyperedges and incorporate the market contextual features into the process of individual trajectory modeling. For learning the trajectory-enhanced market representations, we aggregate the message from hyperedges associated with a specific node to integrate the fine-grained semantics of trajectories into labor market modeling. Moreover, we design two auxiliary tasks to optimize both intra-scale and cross-scale learning with a self-supervised strategy. Extensive experiments on a real-world dataset clearly validate that UniTRep can significantly outperform state-of-the-art baselines for various tasks.

职业流动分析旨在了解不同劳动力市场主体中人才的职业流动模式，从而实现以人才为中心的广泛应用，例如工作推荐、劳动力需求预测和公司竞争分析。该领域现有的研究主要集中在单一的固定尺度上，要么考察微观层面的个体轨迹，要么考察宏观层面的市场主体之间的人群流动。因此，人才与劳动力市场之间内在的跨尺度互动在很大程度上被忽视了。为了弥补这一差距，我们提出了UniTRep，一种用于跨尺度职业流动性分析的新颖的统一表示学习框架。具体来说，我们首先引入轨迹超图结构，以低信息丢失的方式组织职业流动模式，其中市场实体和人才轨迹分别表示为节点和超边。然后，为了学习市场意识的人才表示，我们专注地将节点信息传播到超边缘，并将市场背景特征纳入个人轨迹建模的过程中。为了学习轨迹增强的市场表示，我们聚合来自与特定节点相关的超边的消息，以将轨迹的细粒度语义集成到劳动力市场建模中。此外，我们设计了两个辅助任务，通过自我监督策略来优化内部规模和跨规模学习。对真实世界数据集的大量实验清楚地验证了 UniTRep 可以在各种任务中显着优于最先进的基线。