Many of today's applications are based on large-scale networks, having more than billions of nodes/edges. Several other applications have to deal with big data sets: although in principle those massive data sets are not necessarily networks, they often feature a strong linked nature so that they also exhibit an underlying implicit network structure. 

The ambitious goal of AHeAD is to produce new powerful algorithmic tools to handle massive network analytics, providing scientific groundwork and technological advances for processing and visualizing massive, streamed and dynamic networked data. The project will investigate novel algorithmic techniques and will apply the new algorithmic findings especially to the domain of social networks. 

A particular emphasis will be given to the design of effective visualization algorithms that are capable of scaling to networks with millions/billions of entities and links. We believe that this represents a crucial step in the development of powerful visual interfaces, which as of today is still a scientific challenge. 

Achieving those objectives requires a quantum leap in the design and engineering of algorithms: the sheer size of the data as well as their networked and evolving nature pose new algorithmic challenges.