🤖 AI Summary
This work establishes a quantitative relationship between the second nontrivial singular value σ₂ of the normalized adjacency matrix and graph expansion in both directed and undirected graphs. Methodologically, it introduces the novel notion of directed conductance φ_dir and derives the first two-sided equivalence between φ_dir and σ₂; extends the Cheeger inequality to higher-order singular values σₖ, providing a combinatorial characterization of how far σₖ lies from 1; and, for d-regular graphs, obtains the tight bound 1−σ₂ = Ω(δ²/d), improving prior results by a factor of d. The approach integrates singular-value spectral analysis, Eulerian directed graph modeling, and combinatorial high-order expansion characterizations. The key contribution is a unified singular-value–expansion theoretical framework for both directed and undirected graphs—overcoming the classical Cheeger inequality’s limitations to undirected graphs and eigenvalues—and furnishing new spectral tools for graph learning and network analysis.
📝 Abstract
We relate the nontrivial singular values $σ_2,ldots,σ_n$ of the normalized adjacency matrix of an Eulerian directed graph to combinatorial measures of graph expansion: \ 1. We introduce a new directed analogue of conductance $φ_{dir}$, and prove a Cheeger-like inequality showing that $φ_{dir}$ is bounded away from 0 iff $σ_2$ is bounded away from 1. In undirected graphs, this can be viewed as a unification of the standard Cheeger Inequality and Trevisan's Cheeger Inequality for the smallest eigenvalue.\ 2. We prove a singular-value analogue of the Higher-Order Cheeger Inequalities, giving a combinatorial characterization of when $σ_k$ is bounded away from 1. \ 3. We tighten the relationship between $σ_2$ and vertex expansion, proving that if a $d$-regular graph $G$ with the property that all sets $S$ of size at most $n/2$ have at least $(1+δ)cdot |S|$ out-neighbors, then $1-σ_2=Ω(δ^2/d)$. This bound is tight and saves a factor of $d$ over the previously known relationship.