Anomaly detection is a challenging task, particularly in systems with many variables. Anomalies are outliers that statistically differ from the analyzed data and can arise from rare events, malfunctions, or system misuse. This study investigated the ability to detect anomalies in global financial markets through Graph Neural Networks (GNN) considering an uncertainty scenario measured by a nonextensive entropy. The main findings show that the complex structure of highly correlated assets decreases in a crisis, and the number of anomalies is statistically different for nonextensive entropy parameters considering before, during, and after crisis.

翻译：异常检测是一项具有挑战性的任务，尤其是在涉及大量变量的系统中。异常是在统计上与所分析数据存在差异的离群点，可能源于罕见事件、系统故障或系统滥用。本研究探讨了通过图神经网络在考虑非广延熵所衡量的不确定性场景下，检测全球金融市场异常的能力。主要研究发现表明，高度相关资产的复杂结构在危机期间会减弱，且针对危机前、危机中及危机后阶段，非广延熵参数下的异常数量在统计上存在显著差异。