Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Graphs are used widely to model complex systems, and detecting anomalies in a graph is
an important task in the analysis of complex systems. Graph anomalies are patterns in a …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a
variety of graph-related applications. However, the performance of GNNs can be …

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-based anomaly detection has been widely used for detecting malicious activities in
real-world applications. Existing attempts to address this problem have thus far focused on …

Detecting abnormal nodes from attributed networks is of great importance in many real
applications, such as financial fraud detection and cyber security. This task is challenging …

Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern
and feature information are different from most normal graphs in a graph set, which is rarely …

Anomaly analytics is a popular and vital task in various research contexts that has been
studied for several decades. At the same time, deep learning has shown its capacity in …

We reveal a one-class homophily phenomenon, which is one prevalent property we find
empirically in real-world graph anomaly detection (GAD) datasets, ie, normal nodes tend to …