Abstract
Social link recommendation systems, like “Peopleyou-may-know” on Facebook, “Who-to-follow” on
Twitter, and “Suggested-Accounts” on Instagram
assist the users of a social network in establishing
new connections with other users. While these systems are becoming more and more important in the
growth of social media, they tend to increase the
popularity of users that are already popular. Indeed,
since link recommenders aim at predicting users’
behavior, they accelerate the creation of links that
are likely to be created in the future, and, as a consequence, they reinforce social biases by suggesting few (popular) users, while giving few chances
to the majority of users to build new connections
and increase their popularity.
In this paper we measure the popularity of a user
by means of its social influence, which is its capability to influence other users’ opinions, and we
propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood
of being created. In detail, we give a constant factor
approximation algorithm for the problem of maximizing the social influence of a given set of target
users by suggesting a fixed number of new connections. We experimentally show that, with few new
links and small computational time, our algorithm
is able to increase by far the social influence of the
target users. We compare our algorithm with several baselines and show that it is the most effective
one in terms of increased influence