Spectral Normalized-Cut Graph Partitioning with Fairness Constraints
Jia Li Yanhao Wang Arpit Merchant
Normalized-cut graph partitioning aims to divide the set of nodes in a graph
into $k$ disjoint clusters to minimize the fraction of the total edges between
any cluster and all other clusters. In this paper, we consider a fair variant
of the partitioning problem wherein nodes are characterized by a categorical
sensitive attribute (e.g., gender or race) indicating membership to different
demographic groups. Our goal is to ensure that each group is approximately
proportionally represented in each cluster while minimizing the normalized cut
value. To resolve this problem, we propose a two-phase spectral algorithm
called FNM. In the first phase, we add an augmented Lagrangian term based on
our fairness criteria to the objective function for obtaining a fairer spectral
node embedding. Then, in the second phase, we design a rounding scheme to
produce $k$ clusters from the fair embedding that effectively trades off
fairness and partition quality. Through comprehensive experiments on nine
benchmark datasets, we demonstrate the superior performance of FNM compared
with three baseline methods.