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Cheeger Inequalities for Vertex Expansion and Reweighted Eigenvalues

arXiv 2203.06168 ↗First posted 11 Mar 2022cs.DS cs.DM math.COcited by 4

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Abstract (arXiv)

The classical Cheeger's inequality relates the edge conductance φ of a graph and the second smallest eigenvalue λ2 of the Laplacian matrix. Recently, Olesker-Taylor and Zanetti discovered a Cheeger-type inequality psi2 / log |V| lesssim λ2^* lesssim psi connecting the vertex expansion psi of a graph G=(V,E) and the maximum reweighted second smallest eigenvalue λ2^* of the Laplacian matrix. In this work, we first improve their result to psi2 / log d lesssim λ2^* lesssim psi where d is the maximum degree in G, which is optimal assuming the small-set expansion conjecture. Also, the improved result holds for weighted vertex expansion, answering an open question by Olesker-Taylor and Zanetti. Building on this connection, we then develop a new spectral theory for vertex expansion. We discover that several interesting generalizations of Cheeger inequalities relating edge conductances and eigenvalues have a close analog in relating vertex expansions and reweighted eigenvalues. These include an analog of Trevisan's result on bipartiteness, an analog of higher order Cheeger's inequality, and an analog of improved Cheeger's inequality. Finally, inspired by this connection, we present negative evidence to the 0/1-polytope edge expansion conjecture by Mihail and Vazirani. We construct 0/1-polytopes whose graphs have very poor vertex expansion. This implies that the fastest mixing time to the uniform distribution on the vertices of these 0/1-polytopes is almost linear in the graph size. This does not provide a counterexample to the conjecture, but this is in contrast with known positive results which proved poly-logarithmic mixing time to the uniform distribution on the vertices of subclasses of 0/1-polytopes.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2022.

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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

4 papers.