CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs

Unsupervised Representation Learning on graphs is gaining gorithms on downstream tasks. Accordingly, fairness in the context traction due to the increasing abundance of unlabelled network of trained decision-making systems has increased in popularity recently data and the compactness, richness, and usefulness of the representations due to the numerous social distresses caused when systems generated. In this context, the need to consider fairness and that did not incorporate adequate fairness measures were deployed bias constraints while generating the representations has been wellmotivated in the wild [29, 25]. The job platform XING is an extreme example and studied to some extent in prior works. One major limitation that exhibited gender-based discrimination [4]. of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centralityaware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks.