Graph Based Anomaly

Graph-based anomaly detection aims to identify unusual patterns or outliers within data represented as graphs, focusing on nodes, edges, or subgraphs. Current research heavily utilizes graph neural networks (GNNs), including variations like graph convolutional networks and graph attention networks, often incorporating techniques like contextual matrix profiles and persistent homology optimization to improve accuracy and efficiency. This field is significant for its broad applicability across diverse domains, such as financial fraud detection, system monitoring, and healthcare, enabling more robust and insightful anomaly detection in complex relational data.