Fairwalk: Towards Fair Graph Embedding
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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.
2019 ◽
Vol 2019
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pp. 1-9
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2020 ◽
Vol 34
(04)
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pp. 6837-6844
2013 ◽
Vol 24
(04)
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pp. 1350022
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