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 …

In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in
various applications, from recommender systems and information retrieval to healthcare and …

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-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

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

Graph anomaly detection (GAD) suffers from heterophily—abnormal nodes are sparse so
that they are connected to vast normal nodes. The current solutions upon Graph Neural …

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

Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such
as social network analysis, financial fraud detection, and drug discovery. As a central …

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in
various applications, from recommender systems and information retrieval to healthcare and …