Career Mobility Analysis With Uncertainty-Aware Graph Autoencoders: A Job Title Transition Perspective

Abstract

Career mobility analysis aims at discovering the movement patterns of employees across different job positions or grades, which can benefit various human resource-related applications. Indeed, recent studies in this direction mainly focus on modeling individual career trajectories, while the macroguidance for labor market assessment has been largely ignored. To this end, in this article, we propose to study career mobility from a market-driven perspective based on large-scale online professional networks (OPNs). Specifically, we propose an uncertainty-aware graph autoencoders (UnGAEs) framework, which can simultaneously discover potential job title transition patterns and predict job durations. In this phase, we first construct a job title transition graph based on massive career trajectory data from OPNs. Then, considering the inherent uncertainty in career mobility, we introduce a novel uncertainty-aware graph encoder (UnGE) to represent job titles as Gaussian embeddings. Furthermore, we design two task-specific decoders that can preserve the asymmetric relationships between job titles, namely the gravity-inspired decoder (GID) and the energy-inspired decoder (EID), for predicting potential transition patterns and corresponding duration, respectively. In particular, both tasks are modeled through a specially designed multitask learning approach. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of UnGAE compared with state-of-the-art baselines, as well as some potential applications such as job title benchmarking and career path planning.