Expander Decomposition for Non-Uniform Vertex Measures
Daniel Agassy, Dani Dorfman, Haim Kaplan
TL;DR
This work generalizes expander decomposition to non-uniform vertex measures by introducing μ-expansion and developing a randomized \\tilde{O}(m) time algorithm that computes a $(\\phi,\\phi\\log^2 n \\cdot \\frac{\\mu(V)}{m})$-expander decomposition with respect to μ-expansion. Building on the Cut-Matching framework, it adapts a spectral, fractional-matching approach to handle μ-weighted cuts, embedding, and congestion through a self-contained analysis that culminates in a near-μ-expander residual. The core technical contributions include a μ-expansion–oriented cut-matching game with a cut player and a matching player, a potential-based analysis of convergence, and an integration step (trimming) that yields a full expander decomposition in \\tilde{O}(m) time. The results extend the applicability of expander decompositions to graphs with non-uniform vertex weights, enabling more general algorithmic applications where vertex significance varies substantially.
Abstract
A $(φ,ε)$-expander-decomposition of a graph $G$ (with $n$ vertices and $m$ edges) is a partition of $V$ into clusters $V_1,\ldots,V_k$ with conductance $Φ(G[V_i]) \ge φ$, such that there are $O(εm)$ inter-cluster edges. Such a decomposition plays a crucial role in many graph algorithms. [Agassy, Dorman, and Kaplan, ICALP 2023] (ADK) gave a randomized $\tilde{O}(m)$ time algorithm for computing a $(φ, φ\log^2 {n})$-expander decomposition. In this paper we generalize this result for a broader notion of expansion. Let $μ\in \mathbb{R}_{\ge 0 }^n$ be a vertex measure. A standard generalization of conductance of a cut $(S,\overline{S})$ is its $μ$-expansion $Φ^μ_G(S,\overline{S}) = |E(S,\overline{S})|/\min \{μ(S),μ(\overline{S})\}$, where $μ(S) = \sum_{v\in S} μ(v)$. We present a randomized $\tilde{O}(m)$ time algorithm for computing a $(φ, φ\log^2 {n}\cdot\frac{μ(V)}{m})$-expander decomposition with respect to $μ$-expansion. A substantial portion of the exposition is adapted from ADK, and this work serves as a convenient reference for generalized expander decomposition.