This work aims to reproduce the findings of the paper "Fair Attribute Completion on Graph with Missing Attributes" written by Guo, Chu, and Li arXiv:2302.12977 by investigating the claims made in the paper. This paper suggests that the results of the original paper are reproducible and thus, the claims hold. However, the claim that FairAC is a generic framework for many downstream tasks is very broad and could therefore only be partially tested. Moreover, we show that FairAC is generalizable to various datasets and sensitive attributes and show evidence that the improvement in group fairness of the FairAC framework does not come at the expense of individual fairness. Lastly, the codebase of FairAC has been refactored and is now easily applicable for various datasets and models.

翻译：本研究旨在复现Guo、Chu和Li在arXiv:2302.12977中发表的论文《基于缺失属性的图公平属性补全》的发现，通过考察该论文提出的各项主张。本文表明原始论文的结果具有可复现性，因此其主张成立。然而，FairAC是一个适用于多种下游任务的通用框架这一主张过于宽泛，因此仅能进行部分验证。此外，我们证明FairAC能够泛化到不同数据集和敏感属性，并展示证据表明FairAC框架在提升群体公平性的同时并未牺牲个体公平性。最后，我们对FairAC的代码库进行了重构，使其现在能够便捷地应用于各类数据集和模型。