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.

我们提出了一种用于联合技能需求 - 供应预测的跨视图分层图学习超网络（CHGH）框架，通过整合历史的需求 - 供应差距来联合预测需求和供应变化，实验证明该框架相对于七个对照模型更为优越，证明了三个模块的有效性。