# Labor Migration Modeling through Large-scale Job Query Data

arXiv: https://arxiv.org/abs/2410.02639 · HTML: https://mlsanity.com/paper/2410.02639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Graph Neural Networks for Graphs with Heterophily: A Survey](https://mlsanity.com/paper/2202.07082/) (2022)
- [Non-Local Graph Neural Networks](https://mlsanity.com/paper/2005.14612/) (2020)
- [A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction](https://mlsanity.com/paper/2401.17838/) (2024)

## Abstract (arXiv)

Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, existing studies on labor migration largely rely on limited survey data with statistical methods, which fail to deliver timely and fine-grained insights for time-varying regional trends. To this end, we propose a deep learning-based spatial-temporal labor migration analysis framework, DHG-SIL, by leveraging large-scale job query data. Specifically, we first acquire labor migration intention as a proxy of labor migration via job queries from one of the world's largest search engines. Then, a Disprepant Homophily co-preserved Graph Convolutional Network (DH-GCN) and an interpretable temporal module are respectively proposed to capture cross-city and sequential labor migration dependencies. Besides, we introduce four interpretable variables to quantify city migration properties, which are co-optimized with city representations via tailor-designed contrastive losses. Extensive experiments on three real-world datasets demonstrate the superiority of our DHG-SIL. Notably, DHG-SIL has been deployed as a core component of a cooperative partner's intelligent human resource system, and the system supported a series of city talent attraction reports.