A great variety of systems in nature, society and technology–from the web of sexual contacts
to the Internet, from the nervous system to power grids–can be modeled as graphs of …

The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Networks are a fundamental tool for modeling complex systems in a variety of domains
including social and communication networks as well as biology and neuroscience. The …

Network embedding, which aims to learn the low-dimensional representations of vertices, is
an important task and has attracted considerable research efforts recently. In real world …

Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

Social network based applications have experienced exponential growth in recent years.
One of the reasons for this rise is that this application domain offers a particularly fertile …

The expansion of the web and emergence of a large number of social networking sites
(SNS) have empowered users to easily interconnect on a shared platform. A social network …

Real-world social networks from a variety of domains can naturally be modelled as dynamic
graphs. However, approaches to detecting communities have largely focused on identifying …

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 …