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Tackling unfairness in graph learning models is a challenging task, as theunfairness issues on graphs involve both attributes and topological structures.Existing work on fair graph learning simply assumes that attributes of allnodes are available for model training and then makes fair predictions. Inpractice, however, the attributes of some nodes might not be accessible due tomissing data or privacy concerns, which makes fair graph learning even morechallenging. In this paper, we propose FairAC, a fair attribute completionmethod, to complement missing information and learn fair node embeddings forgraphs with missing attributes. FairAC adopts an attention mechanism to dealwith the attribute missing problem and meanwhile, it mitigates two types ofunfairness, i.e., feature unfairness from attributes and topological unfairnessdue to attribute completion. FairAC can work on various types of homogeneousgraphs and generate fair embeddings for them and thus can be applied to mostdownstream tasks to improve their fairness performance. To our best knowledge,FairAC is the first method that jointly addresses the graph attributioncompletion and graph unfairness problems. Experimental results on benchmarkdatasets show that our method achieves better fairness performance with lesssacrifice in accuracy, compared with the state-of-the-art methods of fair graphlearning. Code is available at: https://github.com/donglgcn/FairAC.

Fair Attribute Completion on Graph with Missing Attributes