  Node representation learning has demonstrated its efficacy for various
applications on graphs, which leads to increasing attention towards the area.
However, fairness is a largely under-explored territory within the field, which
may lead to biased results towards underrepresented groups in ensuing tasks. To
this end, this work theoretically explains the sources of bias in node
representations obtained via Graph Neural Networks (GNNs). Our analysis reveals
that both nodal features and graph structure lead to bias in the obtained
representations. Building upon the analysis, fairness-aware data augmentation
frameworks on nodal features and graph structure are developed to reduce the
intrinsic bias. Our analysis and proposed schemes can be readily employed to
enhance the fairness of various GNN-based learning mechanisms. Extensive
experiments on node classification and link prediction are carried out over
real networks in the context of graph contrastive learning. Comparison with
multiple benchmarks demonstrates that the proposed augmentation strategies can
improve fairness in terms of statistical parity and equal opportunity, while
providing comparable utility to state-of-the-art contrastive methods.
