  The link recommendation problem consists in suggesting a set of links to the
users of a social network in order to increase their social circles and the
connectivity of the network. Link recommendation is extensively studied in the
context of social networks and of general complex networks due to its wide
range of applications. Most of the existing link recommendation methods
estimate the likelihood that a link is adopted by users and recommend links
that are likely to be established. However, most of such methods overlook the
impact that the suggested links have on the capability of the network to spread
information. Indeed, such capability is directly correlated with both the
engagement of a single user and the revenue of online social networks. In this
paper, we study link recommendation systems from the point of view of
information diffusion. In detail, we consider the problem in which we are
allowed to spend a given budget to create new links so to suggest a bounded
number of possible persons to whom become friend in order to maximize the
influence of a given set of nodes. We model the influence diffusion in a
network with the popular Independent Cascade model.
