Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

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 …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

Graph representation learning resurges as a trending research subject owing to the
widespread use of deep learning for Euclidean data, which inspire various creative designs …

Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …

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

Representation Learning over graph structured data has received significant attention
recently due to its ubiquitous applicability. However, most advancements have been made …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …