A Comprehensive Review of Attention-Based Sparse Graph Convolutional Neural Network -Based Forecast Model for Career Planning in Human Resource Management

Abstract

The rapid evolution of artificial intelligence has significantly transformed decision-making processes in human resource management, particularly in career planning and workforce analytics. Traditional models often fail to capture complex interdependencies among employees, skills, and organizational structures. In response, attention-based sparse graph convolutional neural networks have emerged as a powerful paradigm for modeling relational data and forecasting career trajectories. This paper presents a comprehensive review of recent advancements in attention-driven sparse graph convolutional neural network-based forecast models applied to career planning. The study explores how graph structures represent employee relationships, skill transitions, and organizational hierarchies while attention mechanisms enhance interpretability and feature importance learning. Sparse modeling further improves computational efficiency and scalability in large HR datasets. The review synthesizes existing literature, highlighting methodologies, datasets, performance metrics, and practical implications. It also identifies key challenges such as data sparsity, privacy concerns, and model interpretability. Furthermore, emerging trends including hybrid deep learning frameworks, explainable AI, and real-time HR analytics are discussed. The findings suggest that attention-based sparse graph convolutional models provide superior predictive capabilities and strategic insights compared to conventional approaches. This paper serves as a foundational reference for researchers and practitioners aiming to leverage advanced AI techniques for intelligent career planning and workforce optimization.