Network-Informed Optimal Control via Graph Neural Networks: A Framework with Application to Tax Enforcement

Nguemfouo, Marcial
Bossale, Pierre Raymond

JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Vol. 10 No. 1 (2026): February 2026

This paper introduces a novel framework integrating multiplex network theory, machine learning, and optimal control to optimize tax revenue dynamics in the Democratic Republic of Congo (DRC). We model the Congolese economy as a multiplex network where economic sectors represent interdependent layers. Using machine learning techniques on empirical tax data (2000-2024), we reconstruct network topology and identify systemic sectors. Our network informed optimal control approach demonstrates potential revenue increases of 25-35% with 30-40% volatility reduction. The framework provides actionable insights for the upcoming transition to Corporate Income Tax (CIT) and offers a replicable methodology for developing economies.