Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

Anomaly detection is an important problem with multiple applications, and thus has been
studied for decades in various research domains. In the past decade there has been a …

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 …

The study of network robustness is a critical tool in the characterization and sense making of
complex interconnected systems such as infrastructure, communication and social networks …

Network robustness is critical for various industrial and social networks against malicious
attacks, which has various meanings in different research contexts and here it refers to the …

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 …

Given a stream of entries in a multi-aspect data setting ie, entries having multiple
dimensions, how can we detect anomalous activities in an unsupervised manner? For …

Spectral measures have long been used to quantify the robustness of real-world graphs. For
example, spectral radius (or the principal eigenvalue) is related to the effective spreading …

Network robustness refers to the ability of a network to continue its functioning against
malicious attacks, which is critical for various natural and industrial networks. Network …

Community detection is expensive, and the cost generally depends at least linearly on the
number of vertices in the graph. We propose working with a reduced graph that has many …