Tackling unfairness in graph learning models is a challenging task, as the
unfairness issues on graphs involve both attributes and topological structures.
Existing work on fair graph learning simply assumes that attributes of all
nodes are available for model training and then makes fair predictions. In
practice, however, the attributes of some nodes might not be accessible due to
missing data or privacy concerns, which makes fair graph learning even more
challenging. In this paper, we propose FairAC, a fair attribute completion
method, to complement missing information and learn fair node embeddings for
graphs with missing attributes. FairAC adopts an attention mechanism to deal
with the attribute missing problem and meanwhile, it mitigates two types of
unfairness, i.e., feature unfairness from attributes and topological unfairness
due to attribute completion. FairAC can work on various types of homogeneous
graphs and generate fair embeddings for them and thus can be applied to most
downstream tasks to improve their fairness performance. To our best knowledge,
FairAC is the first method that jointly addresses the graph attribution
completion and graph unfairness problems. Experimental results on benchmark
datasets show that our method achieves better fairness performance with less
sacrifice in accuracy, compared with the state-of-the-art methods of fair graph
learning. Code is available at: this https URL.

本文提出了一种名为 FairAC 的公平属性补全方法，采用注意力机制来处理属性丢失问题，并同时减少因属性完整性而导致的两种不公平性，通过在基准数据集上进行实验，结果表明我们的方法在公平性性能方面比公平图学习的现有方法表现更好且牺牲较少的准确性。