  Fairness has been taken as a critical metric in machine learning models,
which is considered as an important component of trustworthy machine learning.
In this paper, we focus on obtaining fairness for popular link prediction
tasks, which are measured by dyadic fairness. A novel pre-processing
methodology is proposed to establish dyadic fairness through data repairing
based on optimal transport theory. With the well-established theoretical
connection between the dyadic fairness for graph link prediction and a
conditional distribution alignment problem, the dyadic repairing scheme can be
equivalently transformed into a conditional distribution alignment problem.
Furthermore, an optimal transport-based dyadic fairness algorithm called
DyadicOT is obtained by efficiently solving the alignment problem, satisfying
flexibility and unambiguity requirements. The proposed DyadicOT algorithm shows
superior results in obtaining fairness compared to other fairness methods on
two benchmark graph datasets.
