Anomalous Graph

Anomalous graph detection focuses on identifying graphs that deviate significantly from a set of normal graphs, a crucial task with applications ranging from fraud detection to medical diagnosis. Current research emphasizes unsupervised methods, often employing graph neural networks (GNNs) like graph autoencoders and graph transformers, to learn representations that effectively distinguish anomalies, with a growing interest in incorporating spectral graph properties and addressing class imbalance. These advancements improve the accuracy and explainability of anomaly detection, leading to more reliable insights in various domains and driving further development of robust and generalizable models.