Node-Level KPI Regression over Graph-Structured Mobile Network Data: An Empirical Investigation - Santos, Guilherme F. R.; Nascimento Junior, Amadeu do (Anais do Workshop de Inteligência Artificial para Redes de Computadores (WIARC); 2026: Anais do I Workshop de Inteligência Artificial para Redes de ComputadoresDO - 10.5753/wiarc.2026.22942, 2026)
6G RAN management demands autonomous solutions translating high-level QoS/QoE objectives into configuration changes with measurable KPI impact. We address KPI prediction from configuration adjustments—a problem largely underexplored outside temporal settings—framed as node-level regression on graph-structured data. Through a principled ablation study, we show that lightweight GNNs outperform flat baselines by capturing topological dependencies, that KPI responses are predominantly local, and that residual connections with dedicated MLP blocks yield consistent gains, while self-supervised pretraining fails to transfer to this setting. Our results provide an empirical foundation for AI/ML-driven autonomous next-generation RAN management.