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 …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …

Graph-based anomaly detection (GBAD) approaches are among the most popular
techniques used to analyze connectivity patterns in communication networks and identify …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

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 …

Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

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 …