A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
This work addresses the need for employees and employers to anticipate skill shifts in the labor market, but it is incremental as it builds on graph learning methods for a specific domain.
The paper tackles the problem of predicting dynamic skill demand and supply in the labor market by proposing a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework, which outperforms seven baselines on three real-world datasets.
The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.