English

Higher-Order Cheeger Inequality for Partitioning with Buffers

Data Structures and Algorithms 2023-08-22 v1

Abstract

We prove a new generalization of the higher-order Cheeger inequality for partitioning with buffers. Consider a graph G=(V,E)G=(V,E). The buffered expansion of a set SVS \subseteq V with a buffer BVSB \subseteq V \setminus S is the edge expansion of SS after removing all the edges from set SS to its buffer BB. An ε\varepsilon-buffered kk-partitioning is a partitioning of a graph into disjoint components PiP_i and buffers BiB_i, in which the size of buffer BiB_i for PiP_i is small relative to the size of PiP_i: BiεPi|B_i| \le \varepsilon |P_i|. The buffered expansion of a buffered partition is the maximum of buffered expansions of the kk sets PiP_i with buffers BiB_i. Let hGk,εh^{k,\varepsilon}_G be the buffered expansion of the optimal ε\varepsilon-buffered kk-partitioning, then for every δ>0\delta>0, hGk,εOδ(1)(logkε)λ(1+δ)k,h_G^{k,\varepsilon} \le O_\delta(1) \cdot \Big( \frac{\log k}{ \varepsilon}\Big) \cdot \lambda_{\lfloor (1+\delta) k\rfloor}, where λ(1+δ)k\lambda_{\lfloor (1+\delta)k\rfloor} is the (1+δ)k\lfloor (1+\delta)k\rfloor-th smallest eigenvalue of the normalized Laplacian of GG. Our inequality is constructive and avoids the ``square-root loss'' that is present in the standard Cheeger inequalities (even for k=2k=2). We also provide a complementary lower bound, and a novel generalization to the setting with arbitrary vertex weights and edge costs. Moreover our result implies and generalizes the standard higher-order Cheeger inequalities and another recent Cheeger-type inequality by Kwok, Lau, and Lee (2017) involving robust vertex expansion.

Keywords

Cite

@article{arxiv.2308.10160,
  title  = {Higher-Order Cheeger Inequality for Partitioning with Buffers},
  author = {Konstantin Makarychev and Yury Makarychev and Liren Shan and Aravindan Vijayaraghavan},
  journal= {arXiv preprint arXiv:2308.10160},
  year   = {2023}
}

Comments

45 pages

R2 v1 2026-06-28T11:59:37.078Z