  Despite recent advances in achieving fair representations and predictions
through regularization, adversarial debiasing, and contrastive learning in
graph neural networks (GNNs), the working mechanism (i.e., message passing)
behind GNNs inducing unfairness issue remains unknown. In this work, we
theoretically and experimentally demonstrate that representative aggregation in
message-passing schemes accumulates bias in node representation due to topology
bias induced by graph topology. Thus, a \textsf{F}air \textsf{M}essage
\textsf{P}assing (FMP) scheme is proposed to aggregate useful information from
neighbors but minimize the effect of topology bias in a unified framework
considering graph smoothness and fairness objectives. The proposed FMP is
effective, transparent, and compatible with back-propagation training. An
acceleration approach on gradient calculation is also adopted to improve
algorithm efficiency. Experiments on node classification tasks demonstrate that
the proposed FMP outperforms the state-of-the-art baselines in effectively and
efficiently mitigating bias on three real-world datasets.
