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CrossWalk: Fairness-enhanced Node Representation Learning. (arXiv:2105.02725v2 [cs.LG] UPDATED)
March 28, 2022, 1:12 a.m. | Ahmad Khajehnejad, Moein Khajehnejad, Mahmoudreza Babaei, Krishna P. Gummadi, Adrian Weller, Baharan Mirzasoleiman
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv fairness learning representation representation learning
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