Given a complex graph database of node-and edge-attributed multi-graphs as well as
associated metadata for each graph, how can we spot the anomalous instances? Many real …

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave
differently from the benign ones accounting for the majority of graph-structured instances …

Many anomaly detection applications naturally pro-duce datasets that can be represented
as bipartite graphs (user-interaction-item graphs). These graph datasets are usually sup …

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 …

Graph anomaly detection is attracting remarkable multidisciplinary research interests
ranging from finance, healthcare, and social network analysis. Recent advances on graph …

Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous
nodes from graph-structured data in various domains such as medicine, social networks …

Unsupervised graph anomaly detection is crucial for various practical applications as it aims
to identify anomalies in a graph that exhibit rare patterns deviating significantly from the …

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior
within graphs, benefiting various domains such as fraud detection and social network …

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant
structures and node attributes compared to the majority in a graph set. One primary …

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