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

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 …

Food sharing mobile applications are becoming increasingly popular, but little is known
about the new social configurations of people using them, particularly those applications that …

Controlling the dissemination of an entity (eg, meme, virus, etc) on a large graph is an
interesting problem in many disciplines. Examples include epidemiology, computer security …

Detecting botnets in a network is crucial because bots impact numerous areas such as cyber
security, finance, health care, law enforcement, and more. Botnets are becoming more …

With the emergence of data science, graph computing is becoming a crucial tool for
processing big connected data. Although efficient implementations of specific graph …

Graphs have been widely adopted to accomplish fraud detection tasks because of their
inherently favorable structure to capture the intricate features in many complicated …

The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of
heterogeneous data. Thus, traditional approaches quickly became unpractical for real life …

The rapid spread of infectious diseases and online rumors share similarities in terms of their
speed, scale, and patterns of contagion. Although these two phenomena have historically …

In an era where accumulating data is easy and storing it inexpensive, feature selection plays
a central role in helping to reduce the high-dimensionality of huge amounts of otherwise …