(2019) Fairwalk: Towards Fair Graph Embedding.
In: International Joint Conference on Artificial Intelligence.
Conference:
IJCAI International Joint Conference on Artificial Intelligence
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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.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Divisions: | Yang Zhang (YZ) |
| Conference: | IJCAI International Joint Conference on Artificial Intelligence |
| Depositing User: | Yang Zhang |
| Date Deposited: | 04 Oct 2019 10:04 |
| Last Modified: | 11 May 2021 10:40 |
| Primary Research Area: | NRA1: Trustworthy Information Processing |
| URI: | https://publications.cispa.saarland/id/eprint/2933 |
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