Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Evolutionary network analysis has found an increasing interest in the literature because of
the importance of different kinds of dynamic social networks, email networks, biological …

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 …

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

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 …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Given a stream of heterogeneous graphs containing different types of nodes and edges,
how can we spot anomalous ones in real-time while consuming bounded memory? This …

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …

The widespread deployment of smartphones, net-worked in-vehicle devices with geo-
positioning capabilities, and vessel tracking technologies renders it feasible to collect the …

A number of sensor applications in recent years collect data which can be directly
associated with human interactions. Some examples of such applications include GPS …