Anomaly Node

Anomaly node detection focuses on identifying nodes in a graph exhibiting behavior significantly different from the majority. Current research emphasizes developing robust methods, often employing graph neural networks (GNNs), generative models, and contrastive learning techniques, to overcome challenges like imbalanced datasets and the influence of anomalous nodes on network representations. These advancements are crucial for various applications, including fraud detection, cybersecurity, and medical diagnosis, where identifying outliers within complex relational data is critical for improved decision-making and system reliability. Recent work also highlights the importance of incorporating both structural and attribute information for more accurate anomaly detection.