  Algorithmic fairness has attracted significant attention in the past years.
Surprisingly, there is little work on fairness in networks. In this work, we
consider fairness for link analysis algorithms and in particular for the
celebrated PageRank algorithm. We provide definitions for fairness, and propose
two approaches for achieving fairness. The first modifies the jump vector of
the Pagerank algorithm to enfonce fairness, and the second imposes a fair
behavior per node. We also consider the problem of achieving fairness while
minimizing the utility loss with respect to the original algorithm. We present
experiments with real and synthetic graphs that examine the fairness of
Pagerank and demonstrate qualitatively and quantitatively the properties of our
algorithms.
