  The potential for machine learning systems to amplify social inequities and
unfairness is receiving increasing popular and academic attention. Much recent
work has focused on developing algorithmic tools to assess and mitigate such
unfairness. However, there is little work on enhancing fairness in graph
algorithms. Here, we develop a simple, effective and general method, CrossWalk,
that enhances fairness of various graph algorithms, including influence
maximization, link prediction and node classification, applied to node
embeddings. CrossWalk is applicable to any random walk based node
representation learning algorithm, such as DeepWalk and Node2Vec. The key idea
is to bias random walks to cross group boundaries, by upweighting edges which
(1) are closer to the groups' peripheries or (2) connect different groups in
the network. CrossWalk pulls nodes that are near groups' peripheries towards
their neighbors from other groups in the embedding space, while preserving the
necessary structural properties of the graph. Extensive experiments show the
effectiveness of our algorithm to enhance fairness in various graph algorithms,
including influence maximization, link prediction and node classification in
synthetic and real networks, with only a very small decrease in performance.
