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Induced Minors, Asymptotic Dimension, and Baker's Technique
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Induced Minors, Asymptotic Dimension, and Baker's Technique
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Asymptotic dimension is a large-scale invariant of metric spaces that was introduced by Gromov (1993). We prove that every hereditary class of bounded-degree graphs that excludes some graph as a fat minor has asymptotic dimension at most $2$, which is optimal. This makes substantial progress on a question of Bonamy, Bousquet, Esperet, Groenland, Liu, Pirot, and Scott (J. Eur. Math. Soc. 2023). The key to our proof is a notion inspired by Baker's technique (J. ACM 1994). We say that a graph class $\mathcal{G}$ has bounded Baker-treewidth if there exists a function $f \colon \mathbb{N} \to \mathbb{N}$ such that, for every graph $G\in \mathcal{G}$, there is a layering of $G$ such that the subgraph induced by the union of any $\ell$ consecutive layers has treewidth at most $f(\ell)$. We show that every class of bounded-degree graphs that excludes some graph as an induced minor has bounded Baker-treewidth. We discuss further applications of this result to clustered colouring and the design of linear-time approximate schemes.
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Cited by 1 Pith paper
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Fatness and Flatness
Excluding a fixed graph as a fat minor forces a metric analog of uniform quasi-wideness; this bounds scatter dimension and yields EPAS-style approximation for norm k-clustering.
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