Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints

Fairness in clustering is computationally expensive: Current methods that ensure fair representation of protected groups in clustering algorithms are slow, motivating the need for faster approaches that don't sacrifice fairness outcomes.

A new algorithm (Fair-SMW) doubles computational speed: The researchers developed Fair-SMW, which achieves clustering results "twice as fast as the state-of-the-art" while maintaining comparable fairness in group representation, making fair clustering more practical for real-world applications.

Trade-offs between speed and fairness can be flexible: The algorithm offers flexibility to either match existing fairness levels at higher speeds, or achieve "twice as much balance" (better proportional representation of protected groups) if computational time is less constrained.

Multiple technical approaches can achieve similar fairness goals: By testing three different mathematical alternatives to standard methods, the research demonstrates that there are multiple pathways to fair clustering, suggesting fairness constraints don't require a single rigid approach.

Real-world datasets show practical improvements: Testing on actual networks (LastFM, Facebook, Deezer, German datasets) rather than just theoretical models demonstrates that these efficiency gains translate to practical benefits for organizations implementing fair clustering systems.