# Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy

arXiv: https://arxiv.org/abs/2308.02914 · HTML: https://mlsanity.com/paper/2308.02914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)

## Built on by (most cited first, 1 of 1)

- [Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2604.24590/) (2026)

## Abstract (arXiv)

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.