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 …

Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

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 …

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 …

Advanced Persistent Threats (APTs) are difficult to detect due to their" low-and-slow" attack
patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

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 …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Embedding a web-scale information network into a low-dimensional vector space facilitates
tasks such as link prediction, classification, and visualization. Past research has addressed …