  Knowledge graph data are prevalent in real-world applications, and knowledge
graph neural networks (KGNNs) are essential techniques for knowledge graph
representation learning. Although KGNN effectively models the structural
information from knowledge graphs, these frameworks amplify the underlying data
bias that leads to discrimination towards certain groups or individuals in
resulting applications. Additionally, as existing debiasing approaches mainly
focus on the entity-wise bias, eliminating the multi-hop relational bias that
pervasively exists in knowledge graphs remains an open question. However, it is
very challenging to eliminate relational bias due to the sparsity of the paths
that generate the bias and the non-linear proximity structure of knowledge
graphs. To tackle the challenges, we propose Fair-KGNN, a KGNN framework that
simultaneously alleviates multi-hop bias and preserves the proximity
information of entity-to-relation in knowledge graphs. The proposed framework
is generalizable to mitigate the relational bias for all types of KGNN. We
develop two instances of Fair-KGNN incorporating with two state-of-the-art KGNN
models, RGCN and CompGCN, to mitigate gender-occupation and nationality-salary
bias. The experiments carried out on three benchmark knowledge graph datasets
demonstrate that the Fair-KGNN can effectively mitigate unfair situations
during representation learning while preserving the predictive performance of
KGNN models.
