Graph neural network for anomaly detection

Abstract

Graph Neural Networks (GNNs) have gained prominence in the realm of anomaly detection on graph-structured data, a critical task in various fields such as cybersecurity, fraud detection, and network monitoring. Unlike traditional anomaly detection methods that often overlook the relational information between data points, GNNs excel by directly incorporating the graph topology along with node and edge attributes into the learning process. This capability enables GNNs to uncover intricate patterns and interactions within the graph that are indicative of anomalous behavior. Through techniques such as node embedding, subgraph analysis, and edge prediction, GNNs can identify deviations from normal patterns in both static and dynamic graphs. Recent advancements have led to the development of specialized GNN architectures and learning strategies tailored for anomaly detection, enhancing the model's sensitivity to subtle anomalies and its ability to generalize across different graph domains. Despite these advances, challenges related to scalability, dynamic graph analysis, and interpretability persist, driving ongoing research efforts. Ultimately, GNNs offer a powerful and nuanced approach for anomaly detection in graph-structured data, promising improved accuracy and efficiency in identifying anomalies across a wide range of applications.