  Content spread inequity is a potential unfairness issue in online social
networks, disparately impacting minority groups. In this paper, we view
friendship suggestion, a common feature in social network platforms, as an
opportunity to achieve an equitable spread of content. In particular, we
propose to suggest a subset of potential edges (currently not existing in the
network but likely to be accepted) that maximizes content spread while
achieving fairness. Instead of re-engineering the existing systems, our
proposal builds a fairness wrapper on top of the existing friendship suggestion
components.
  We prove the problem is NP-hard and inapproximable in polynomial time unless
P = NP. Therefore, allowing relaxation of the fairness constraint, we propose
an algorithm based on LP-relaxation and randomized rounding with fixed
approximation ratios on fairness and content spread. We provide multiple
optimizations, further improving the performance of our algorithm in practice.
Besides, we propose a scalable algorithm that dynamically adds subsets of
nodes, chosen via iterative sampling, and solves smaller problems corresponding
to these nodes. Besides theoretical analysis, we conduct comprehensive
experiments on real and synthetic data sets. Across different settings, our
algorithms found solutions with nearzero unfairness while significantly
increasing the content spread. Our scalable algorithm could process a graph
with half a million nodes on a single machine, reducing the unfairness to
around 0.0004 while lifting content spread by 43%.
