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 …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Anomaly detection on attributed networks attracts considerable research interests due to
wide applications of attributed networks in modeling a wide range of complex systems …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

Attributed networks are ubiquitous and form a critical component of modern information
infrastructure, where additional node attributes complement the raw network structure in …

Massive and dynamic networks arise in many practical applications such as social media,
security and public health. Given an evolutionary network, it is crucial to detect structural …

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

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

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 …

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