Fairness in Graph Machine Learning: Recent Advances and Future Prospectives

Graph machine learning algorithms have become popular tools in helping us gain a deeper understanding of the ubiquitous graph data. Despite their effectiveness, most graph machine learning algorithms lack considerations for fairness, which can result in discriminatory outcomes against certain demographic subgroups or individuals. As a result, there is a growing societal concern about mitigating the bias exhibited in these algorithms. To tackle the problem of algorithmic bias in graph machine learning algorithms, this tutorial aims to provide a comprehensive overview of recent research progress in measuring and mitigating the bias in machine learning algorithms on graphs. Specifically, this tutorial first introduces several widely-used fairness notions and the corresponding metrics. Then, we present a well-organized review of the theoretical understanding of bias in graph machine learning algorithms, followed by a summary of existing techniques to debias graph machine learning algorithms. Furthermore, we demonstrate how different real-world applications benefit from these graph machine learning algorithms after debiasing. Finally, we provide insights on current research challenges and open questions to encourage further advances.