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.