Recommending links through influence maximization

Gianlorenzo D'Angelo, Lorenzo Severini, Yllka Velaj

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.

Knowledge Graph



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