Fair-B-PG

Weconsiderfivebenchmarkdatasets-Pokec-z,NBA,Political-Blogs, Cora and Twitter which are predominantlyWeconsiderfivebenchmarkdatasets-Pokec-z,NBA,We consider five benchmark datasets - Pokec-z, NBA, Political-Blogs, Cora and Twitter which are predominantly used in the fair link prediction literature \cite{dong2022fairness}. Since Political-Blogs, Cora and Twitter datasets have a single sensitive attribute we compare the fairness vs. link prediction performance for that case whereas for Pokec-z and NBA datasets we also demonstrate comparison for the case of multiple sensitive attributes. Political-Blogs, Cora and Twitter datasets consist of a single sensitive attribute and hence utility and fairness results could not be obtained for intersectional groups. However, for Pokec and NBA which consists of multiple sensitive attributes, both intersectional and overlapping group fairness results along with utility are reported.    Political-Blogs, Cora and Twitter which are predominantlyused in the fair link prediction literature [10]. Political-Blogs,Cora and Twitter datasets consist of a single sensitive attributeand hence utility and fairness results could not be obtained forintersectional groups. However, for Pokec and NBA whichconsists of multiple sensitive attributes, both intersectionaland overlapping group fairness results along with utility arereported. We assume each node to be associated with a setof features and an (undirected) edge represents a relationshipamong a pair of nodes.used in the fair link prediction literature [10]. Political-Blogs,Cora and Twitter datasets consist of a single sensitive attributeand hence utility and fairness results could not be obtained forintersectional groups. However, for Pokec and NBA whichconsists of multiple sensitive attributes, both intersectionaland overlapping group fairness results along with utility arereported. We assume each node to be associated with a setof features and an (undirected) edge represents a relationshipamong a pair of nodes.

我们选取五个基准数据集——Pokec-z、NBA、Political-Blogs、Cora和Twitter，这些数据集在公平链路预测（fair link prediction）文献中被广泛使用cite{dong2022fairness}。由于Political-Blogs、Cora和Twitter数据集仅包含单一敏感属性，我们针对该场景对比公平性与链路预测性能；而对于Pokec-z和NBA数据集，我们还展示了多敏感属性场景下的对比结果。Political-Blogs、Cora和Twitter数据集仅含单一敏感属性，因此无法获取交叉群体的效用与公平性结果。然而，对于包含多敏感属性的Pokec和NBA数据集，我们报告了交叉群体与重叠群体的公平性结果及效用。我们假设每个节点都关联一组特征，且（无向）边代表节点对之间的关系。