  Machine learning and data mining algorithms have been increasingly used
recently to support decision-making systems in many areas of high societal
importance such as healthcare, education, or security. While being very
efficient in their predictive abilities, the deployed algorithms sometimes tend
to learn an inductive model with a discriminative bias due to the presence of
this latter in the learning sample. This problem gave rise to a new field of
algorithmic fairness where the goal is to correct the discriminative bias
introduced by a certain attribute in order to decorrelate it from the model's
output. In this paper, we study the problem of fairness for the task of edge
prediction in graphs, a largely underinvestigated scenario compared to a more
popular setting of fair classification. To this end, we formulate the problem
of fair edge prediction, analyze it theoretically, and propose an
embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary
graph with a trade-off between the group and individual fairness. We
experimentally show the versatility of our approach and its capacity to provide
explicit control over different notions of fairness and prediction accuracy.
