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 …

Online social networks have received a dramatic increase of interest in the last decade due
to the growth of Internet and Web 2.0. They are among the most popular sites on the Internet …

Anomalies in online social networks can signify irregular, and often illegal behaviour.
Detection of such anomalies has been used to identify malicious individuals, including …

Interactions among people or objects are often dynamic in nature and can be represented
as a sequence of networks, each providing a snapshot of the interactions over a brief period …

Network data is ubiquitous and growing, yet we lack realistic generative network models that
can be calibrated to match real-world data. The recently proposed block two-level Erdös …

Cyber-physical systems have recently emerged in several practical engineering applications
where security and privacy are of paramount importance. This motivated the paper and a …

This paper introduces a novel spectral anomaly detection method by develo** a graph-
based filtering framework. In particular, we consider the problem of unsupervised data …

The ability to detect change-points in a dynamic network or a time series of graphs is an
increasingly important task in many applications of the emerging discipline of graph signal …

Graph signal processing (GSP) deals with the representation, analysis, and processing of
structured data, ie graph signals that are defined on the vertex set of a generic graph. A …

Effectively mining anomalous subgraphs in networks is crucial for many application
scenarios, such as disease outbreak detection, financial fraud detection, and activity …