Transaction Network

Transaction networks, representing relationships between entities in various domains (e.g., financial transactions, cryptocurrency exchanges, social interactions), are analyzed using graph neural networks (GNNs) to identify patterns and anomalies. Current research focuses on developing GNN architectures tailored to the specific characteristics of these networks, such as directed multigraphs and temporal dynamics, often incorporating techniques like federated learning to address privacy concerns and data distribution challenges. These advancements have significant implications for applications like anti-money laundering, fraud detection, and risk management, improving the accuracy and efficiency of these critical tasks.