  Despite the remarkable success of graph neural networks (GNNs) in modeling
graph-structured data, like other machine learning models, GNNs are also
susceptible to making biased predictions based on sensitive attributes, such as
race and gender. For fairness consideration, recent state-of-the-art (SOTA)
methods propose to filter out sensitive information from inputs or
representations, e.g., edge dropping or feature masking. However, we argue that
such filtering-based strategies may also filter out some non-sensitive feature
information, leading to a sub-optimal trade-off between predictive performance
and fairness. To address this issue, we unveil an innovative
neutralization-based paradigm, where additional Fairness-facilitating Features
(F3) are incorporated into node features or representations before message
passing. The F3 are expected to statistically neutralize the sensitive bias in
node representations and provide additional nonsensitive information. We also
provide theoretical explanations for our rationale, concluding that F3 can be
realized by emphasizing the features of each node's heterogeneous neighbors
(neighbors with different sensitive attributes). We name our method as FairSIN,
and present three implementation variants from both data-centric and
model-centric perspectives. Experimental results on five benchmark datasets
with three different GNN backbones show that FairSIN significantly improves
fairness metrics while maintaining high prediction accuracies.
