  People recommender systems may affect the exposure that users receive in
social networking platforms, influencing attention dynamics and potentially
strengthening pre-existing inequalities that disproportionately affect certain
groups.
  In this paper we introduce a model to simulate the feedback loop created by
multiple rounds of interactions between users and a link recommender in a
social network. This allows us to study the long-term consequences of those
particular recommendation algorithms. Our model is equipped with several
parameters to control (i) the level of homophily in the network, (ii) the
relative size of the groups, (iii) the choice among several state-of-the-art
link recommenders, and (iv) the choice among three different user behavior
models, that decide which recommendations are accepted or rejected.
  Our extensive experimentation with the proposed model shows that a minority
group, if homophilic enough, can get a disproportionate advantage in exposure
from all link recommenders. Instead, when it is heterophilic, it gets
under-exposed. Moreover, while the homophily level of the minority affects the
speed of the growth of the disparate exposure, the relative size of the
minority affects the magnitude of the effect. Finally, link recommenders
strengthen exposure inequalities at the individual level, exacerbating the
"rich-get-richer" effect: this happens for both the minority and the majority
class and independently of their level of homophily.
