Recent Advances in Attention-Based Sparse Graph Convolutional Neural Network -Based Forecast Model for Career Planning in Human Resource Management: A Systematic Review

Abstract

The integration of advanced artificial intelligence techniques into Human Resource Management (HRM) has significantly transformed traditional career planning methodologies. Among these advancements, attention-based sparse Graph Convolutional Neural Networks (GCNNs) have emerged as a powerful paradigm for modeling complex relationships within organizational data. This paper presents a systematic review of recent advances in attention-based sparse GCNN-based forecast models specifically designed for career planning applications. The study explores how attention mechanisms enhance interpretability and selective feature importance, while sparsity constraints improve computational efficiency and scalability in large-scale HR datasets. The review synthesizes findings from recent research to highlight key methodological developments, including hybrid architectures, dynamic graph learning, and optimization strategies. Furthermore, the paper examines the role of these models in predicting employee career trajectories, skill evolution, and organizational mobility patterns. Challenges such as data heterogeneity, privacy concerns, and model generalization are critically analyzed. The study also identifies emerging trends, including the integration of explainable AI and reinforcement learning within graph-based HR analytics frameworks. By providing a comprehensive overview, this paper aims to guide researchers and practitioners in leveraging attention-based sparse GCNN models for intelligent and data-driven career planning systems, ultimately contributing to improved workforce management and strategic decision-making in modern organizations.