  Unsupervised Representation Learning on graphs is gaining traction due to the
increasing abundance of unlabelled network data and the compactness, richness,
and usefulness of the representations generated. In this context, the need to
consider fairness and bias constraints while generating the representations has
been well-motivated and studied to some extent in prior works. One major
limitation 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 centrality-aware fairness-inducing framework that leverages the
structural information of graphs to tune the representations generated by
existing frameworks. We deploy it on GraphSAGE (a popular framework in this
domain) and showcase its efficacy on two downstream tasks - Node Classification
and Link Prediction. Empirically, CAFIN consistently reduces the performance
disparity across popular datasets (varying from 18 to 80% reduction in
performance disparity) from various domains while incurring only a minimal cost
of fairness.
