Fraud in public procurement remains a persistent challenge, especially in large, decentralized systems like Brazil's Unified Health System. We introduce Heron's Information Coefficient (HIC), a geometric measure that quantifies how subgraphs deviate from the global structure of a network. Applied to over eight years of Brazilian bidding data for medical supplies, this measure highlights collusive patterns that standard indicators may overlook. Unlike conventional robustness metrics, the Heron coefficient focuses on the interaction between active and inactive subgraphs, revealing structural shifts that may signal coordinated behavior, such as cartel formation. Synthetic experiments support these findings, demonstrating strong detection performance across varying corruption intensities and network sizes. While our results do not replace legal or economic analyses, they offer an effective complementary tool for auditors and policymakers to monitor procurement integrity more effectively. This study demonstrates that simple geometric insight can reveal hidden dynamics in real-world networks better than other Information Theoretic metrics.

翻译：公共采购中的欺诈行为仍然是一个持续存在的挑战，尤其是在巴西统一卫生系统这类大型分散化体系中。我们引入了Heron信息系数（HIC）——一种量化子图如何偏离网络全局结构的几何度量。将该指标应用于巴西八年多的医疗物资招标数据后，它凸显了标准指标可能忽略的合谋模式。与传统稳健性指标不同，Heron系数聚焦于活跃与非活跃子图间的相互作用，揭示了可能暗示协调行为（如卡特尔形成）的结构性转变。合成实验支持了这些发现，证明了该方法在不同腐败强度与网络规模下均具备强大的检测性能。虽然我们的研究结果不能替代法律或经济分析，但它们为审计人员和政策制定者提供了有效的补充工具，以更有效地监控采购廉洁性。本研究表明，简单的几何洞察能够比其他信息论指标更好地揭示现实世界网络中隐藏的动态机制。