Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network
Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches predominantly yield a single point estimate and treat job attributes such as location, occupation, and industry as independent categorical features, ignoring both the inherent uncertainty and multi-modality of real-world compensation data and the rich hierarchical and semantic-similarity relationships that govern pay norms. In this paper we propose GAT-MDN, a unified framework that addresses both limitations simultaneously. For each of the three attribute domains we construct a domain-specific graph whose edges encode (i) hierarchical paren...
| Authors | Zhipei Qin, Mohammad Shokri, N. van Weeren et al. |
|---|---|
| Primary category | cs.SI |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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