A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction

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 relies on domain-expert knowledge or regarding the 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.

Authors

• Wenshuo Chao The Hong Kong University of Science and Technology (Guangzhou) Career Science Lab, BOSS Zhipin
• Zhaopeng Qiu Career Science Lab, BOSS Zhipin
• Likang Wu College of Management and Economics, Tianjin University Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University
• Zhuoning Guo The Hong Kong University of Science and Technology (Guangzhou)
• Zhi Zheng University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence
• Hengshu Zhu Career Science Lab, BOSS Zhipin The Hong Kong University of Science and Technology (Guangzhou)
• Hao Liu The Hong Kong University of Science and Technology (Guangzhou) The Hong Kong University of Science and Technology

Keywords

• DMKM: Graph Mining, Social Network Analysis & Community
• PEAI: AI & Jobs/Labor