Profile and neighbourhood complexity of graphs excluding a minor and tree-structured graphs
Abstract
The r-neighbourhood complexity of a graph G is the function counting, for a given integer k, the largest possible number, over all vertex-subsets A of size k, of subsets of A realized as the intersection between the r-neighbourhood of some vertex and A. A~refinement of this notion is the r-profile complexity, that counts the maximum number of distinct distance-vectors from any vertex to the vertices of A, ignoring distances larger than~r. Typically, in structured graph classes such as graphs of bounded VC-dimension or chordal graphs, these functions are bounded, leading to insights into their structural properties and efficient algorithms. We improve existing bounds on the r-profile complexity (and thus on the r-neighbourhood complexity) for graphs in several structured graph classes. We show that the r-profile complexity of graphs excluding Kh as a minor is in Oh(r3h-3k). For graphs of treewidth at most~t, we give a bound in Ot(rt+1k), which is tight up to a function of~t as a factor. These bounds improve results of Joret and Rambaud and answer a question of their paper [Combinatorica, 2024]. We also apply our methods to other classes of bounded expansion such as graphs excluding a fixed complete graph as a subdivision. For outerplanar graphs, we can improve our treewidth bound by a factor of r and conjecture that a similar improvement holds for graphs with bounded simple treewidth. For graphs of treelength at most~, we give the upper bound of O(k(r2(+1)k)), which we improve to O (k· (r 2k + r2k2) ) in the case of chordal graphs and O(k2r) for interval graphs. Our bounds also imply relations between the order, diameter and metric dimension of graphs in these classes, improving results from [Beaudou et al., SIDMA 2017].
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.