Abstract
Graph embeddings have gained huge popularity in
the recent years as a powerful tool to analyze social networks. However, no prior works have studied potential bias issues inherent within graph embedding. In this paper, we make a first attempt in
this direction. In particular, we concentrate on the
fairness of node2vec, a popular graph embedding
method. Our analyses on two real-world datasets
demonstrate the existence of bias in node2vec when
used for friendship recommendation. We therefore propose a fairness-aware embedding method,
namely Fairwalk, which extends node2vec. Experimental results demonstrate that Fairwalk reduces
bias under multiple fairness metrics while still preserving the utility