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Fairwalk: Towards Fair Graph Embedding

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conference contribution
posted on 2023-11-29, 18:10 authored by Tahleen Rahman, Bartlomiej Surma, Michael BackesMichael Backes, Yang ZhangYang Zhang
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.

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Preferred Citation

Tahleen Rahman, Bartlomiej Surma, Michael Backes and Yang Zhang. Fairwalk: Towards Fair Graph Embedding. In: International Joint Conference on Artificial Intelligence (IJCAI). 2019.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

International Joint Conference on Artificial Intelligence (IJCAI)

Legacy Posted Date

2019-10-04

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_2933, title = "Fairwalk: Towards Fair Graph Embedding", author = "Rahman, Tahleen and Surma, Bartlomiej and Backes, Michael and Zhang, Yang", booktitle="{International Joint Conference on Artificial Intelligence (IJCAI)}", year="2019", }

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