Fairwalk: Towards Fair Graph Embedding
Fairwalk: Towards Fair Graph Embedding
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DOI：
10.24963/ijcai.2019/456
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Tahleen A. Rahman;Bartlomiej Surma;M. Backes;Yang Zhang
中科院分区：
文献类型：
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作者：
Tahleen A. Rahman;Bartlomiej Surma;M. Backes;Yang Zhang
图嵌入作为分析社交网络的有力工具，近年来得到了广泛的应用。然而，没有先前的作品研究了图嵌入中固有的潜在偏差问题。在本文中，我们在这个方向上进行了第一次尝试。特别是，我们集中在node2vec，一个流行的图形嵌入方法的公平性。我们对两个真实世界数据集的分析表明，当用于友谊推荐时，node2vec存在偏差。因此，我们提出了一个公平意识的嵌入方法，即Fairwalk，它扩展了node2vec。实验结果表明，Fairwalk减少偏见下的多个公平性指标，同时仍然保持效用。
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.