Sparsest cut and eigenvalue multiplicities on low degree Abelian Cayley graphs
Tommaso d'Orsi, Chris Jones, Jake Ruotolo, Salil Vadhan, Jiyu Zhang

TL;DR
This paper introduces a spectral algorithm for the Sparsest Cut problem that leverages eigenvalue multiplicities and solution dimension, with applications to low degree Abelian Cayley graphs, achieving near-optimal approximations efficiently.
Contribution
It presents a novel spectral algorithm based on eigenspace enumeration and introduces bounds on solution dimension for Abelian Cayley graphs, extending previous threshold-rank methods.
Findings
Algorithm computes (1+ε)-approximate sparsest cut in polynomial time for certain graphs.
Bound on eigenvalue multiplicity improves previous results, showing tightness.
Collection of approximate sparsest cuts has a small ε-net, aiding in efficient approximation.
Abstract
Whether or not the Sparsest Cut problem admits an efficient -approximation algorithm is a fundamental algorithmic question with connections to geometry and the Unique Games Conjecture. Revisiting spectral algorithms for Sparsest Cut, we present a novel, simple algorithm that combines eigenspace enumeration with a new algorithm for the Cut Improvement problem. The runtime of our algorithm is parametrized by a quantity that we call the solution dimension : the smallest such that the subspace spanned by the first Laplacian eigenvectors contains all but fraction of a sparsest cut. Our algorithm matches the guarantees of prior methods based on the threshold-rank paradigm, while also extending beyond them. To illustrate this, we study its performance on low degree Cayley graphs over Abelian groups -- canonical examples of graphs with poor…
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