Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are
abnormal in their structure and/or the features of their nodes, as compared to other graphs …

Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes are …

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …

Graph anomaly detection, here, aims to find rare patterns that are significantly different from
other nodes. Attributed graphs containing complex structure and attribute information are …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

Anomaly detection on graphs plays a significant role in various domains, including
cybersecurity, e-commerce, and financial fraud detection. However, existing methods on …

Detecting anomalies on graph data has two types of methods. One is pattern mining that
discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks …

Anomaly detection from graph data has drawn much attention due to its practical
significance in many critical applications including cybersecurity, finance, and social …

Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern
and feature information are different from most normal graphs in a graph set, which is rarely …