A Cross-View Hierarchical Graph Learning Hypernetwork for Skill
  Demand-Supply Joint Prediction
Wenshuo Chao Zhaopeng Qiu Likang Wu Zhuoning Guo Zhi Zheng Hengshu Zhu Hao Liu
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.