English

On the Parameterized Complexity of Sparsest Cut and Small-set Expansion Problems

Computational Complexity 2023-04-04 v2

Abstract

We present a parameterized dichotomy for the \textsc{kk-Sparsest Cut} problem in weighted and unweighted versions. In particular, we show that the weighted \textsc{kk-Sparsest Cut} problem is NP-hard for every k3k\geq 3 even on graphs with bounded vertex cover number. Also, the unweighted \textsc{kk-Sparsest Cut} problem is W[1]-hard when parameterized by the three combined parameters tree-depth, feedback vertex set number, and kk. On the positive side, we show that unweighted \textsc{kk-Sparsest Cut} problem is FPT when parameterized by the vertex cover number and kk, and when kk is fixed, it is FPT with respect to the treewidth. Moreover, we show that the generalized version \textsc{kk-Small-Set Expansion} problem is FPT when parameterized by kk and the maximum degree of the graph, though it is W[1]-hard for each of these parameters separately.

Keywords

Cite

@article{arxiv.1910.12353,
  title  = {On the Parameterized Complexity of Sparsest Cut and Small-set Expansion Problems},
  author = {Ramin Javadi and Amir Nikabadi},
  journal= {arXiv preprint arXiv:1910.12353},
  year   = {2023}
}
R2 v1 2026-06-23T11:56:30.480Z