TalentFlow: Explainable Internal Talent Mobility Prediction Using Graph Representation Learning
DOI:
https://doi.org/10.54097/7zspnn42Keywords:
Talent mobility, graph neural networks, heterogeneous graphs, link prediction, explainable AI, human resource analyticsAbstract
Internal talent mobility—the movement of employees into new roles within the same organization—is central to workforce planning, yet most data-driven talent models treat each employee as an isolated feature vector and predict a single binary outcome such as promotion or attrition. This ignores a defining property of mobility: an employee’s next role is shaped not only by individual attributes but by relational context—the skills shared with target roles, the career ladders typical of a function, and the social capital embedded in a managerial and peer network. We present TalentFlow, a framework that reformulates internal mobility as next-role link prediction over a heterogeneous talent graph whose nodes are employees, roles, skills, departments and managers, connected by typed relations. A relational graph convolutional encoder learns employee and role representations by message passing over these relations, and a mobility decoder ranks candidate next roles for each employee. To make recommendations auditable, TalentFlow couples two complementary explanation modalities: SHAP attribution over employee attributes and a structural occlusion method that quantifies how much each relational channel contributed to a recommendation—an explanation flat-feature models cannot provide. Because real internal-mobility trajectories are proprietary, we construct TalentFlow-Sim, a benchmark that anchors employee attribute distributions to the public IBM HR Analytics dataset while generating a mobility layer driven by skill fit, career-ladder priors and managerial sponsorship. Across five seeds, relational message passing improves next-role ranking over strong non-graph baselines and over homogeneous-graph and structure-only embedding methods, and ablations show that removing the managerial/peer relation causes the largest degradation—direct evidence that social-capital signals carry predictive value recoverable only through the graph. TalentFlow complements recent explainable, fairness-aware promotion models such as FairPromote by extending explainable talent analytics from flat classification to relational, structurally grounded mobility prediction.
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