Fair-SMW uses SMW identity and alternative Laplacians to produce group-fair spectral clustering that is twice as fast and twice as balanced as prior methods on SBM and real network data.
Saad, Numerical methods for large eigenvalue problems, Manchester University Press
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints
Fair-SMW uses SMW identity and alternative Laplacians to produce group-fair spectral clustering that is twice as fast and twice as balanced as prior methods on SBM and real network data.