  The importance of understanding and correcting algorithmic bias in machine
learning (ML) has led to an increase in research on fairness in ML, which
typically assumes that the underlying data is independent and identically
distributed (IID). However, in reality, data is often represented using non-IID
graph structures that capture connections among individual units. To address
bias in ML systems, it is crucial to bridge the gap between the traditional
fairness literature designed for IID data and the ubiquity of non-IID graph
data. In this survey, we review such recent advance in fairness amidst non-IID
graph data and identify datasets and evaluation metrics available for future
research. We also point out the limitations of existing work as well as
promising future directions.
