Summary of Labor Migration Modeling Through Large-scale Job Query Data, by Zhuoning Guo et al.
Labor Migration Modeling through Large-scale Job Query Data
by Zhuoning Guo, Le Zhang, Hengshu Zhu, Weijia Zhang, Hui Xiong, Hao Liu
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A deep learning-based spatial-temporal labor migration analysis framework called DHG-SIL is proposed to accurately model labor migration trends for urban governance and commercial applications. By leveraging large-scale job query data from one of the world’s largest search engines, labor migration intention is acquired as a proxy of labor migration. A Disprepant Homophily co-preserved Graph Convolutional Network (DH-GCN) captures cross-city dependencies, while an interpretable temporal module models sequential trends. Four interpretable variables quantify city migration properties and are co-optimized with city representations via tailor-designed contrastive losses. The framework is evaluated on three real-world datasets, demonstrating its superiority over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to study labor migration by using large amounts of job search data from the internet. This helps to understand why people move between cities and what factors influence these decisions. The authors developed a special kind of artificial intelligence that can learn from this data and make predictions about future migration trends. They tested their approach on real-world data and found it was more accurate than other methods. This research has practical applications, such as helping cities attract new workers and develop policies to support economic growth. |
Keywords
» Artificial intelligence » Convolutional network » Deep learning » Gcn
