Fairwalk: Towards Fair Graph Embedding

Tahleen A. Rahman, Bartlomiej Surma, Michael Backes, Yang Zhang

IJCAI 2019 pp. 3289-3295

doi:10.24963/IJCAI.2019/456 /ijcai/2019/rahman2019ijcai-fairwalk/

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.

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Cite

Text

Rahman et al. "Fairwalk: Towards Fair Graph Embedding." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/456

Markdown

[Rahman et al. "Fairwalk: Towards Fair Graph Embedding." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/rahman2019ijcai-fairwalk/) doi:10.24963/IJCAI.2019/456

BibTeX

@inproceedings{rahman2019ijcai-fairwalk,
  title     = {{Fairwalk: Towards Fair Graph Embedding}},
  author    = {Rahman, Tahleen A. and Surma, Bartlomiej and Backes, Michael and Zhang, Yang},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {3289-3295},
  doi       = {10.24963/IJCAI.2019/456},
  url       = {https://mlanthology.org/ijcai/2019/rahman2019ijcai-fairwalk/}
}