mlsanityData to 14 Oct 2026

Finding irrelevant vertices in linear time on bounded-genus graphs

arXiv 1907.05940 ↗First posted 12 Jul 2019cs.DS math.COcited by 1

Not on the map. Why?

Abstract (arXiv)

The irrelevant vertex technique provides a powerful tool for the design of parameterized algorithms for a wide variety of problems on graphs. A common characteristic of these problems, permitting the application of this technique on surface-embedded graphs, is the fact that every graph of large enough treewidth contains a vertex that is irrelevant, in the sense that its removal yields an equivalent instance of the problem. The straightforward application of this technique yields algorithms with running time that is quadratic in the size of the input graph. This running time is due to the fact that it takes linear time to detect one irrelevant vertex and the total number of irrelevant vertices to be detected is linear as well. Using advanced techniques, sub-quadratic algorithms have been designed for particular problems, even in general graphs. However, designing a general framework for linear-time algorithms has been open, even for the bounded-genus case. In this paper we introduce a general framework that enables finding in linear time an entire set of irrelevant vertices whose removal yields a bounded-treewidth graph, provided that the input graph has bounded genus. Our technique consists of decomposing any surface-embedded graph into a tree-structured collection of bounded-treewidth subgraphs where detecting globally irrelevant vertices can be done locally and independently. Our method is applicable to a wide variety of known graph containment or graph modification problems where the irrelevant vertex technique applies. Examples include the (Induced) Minor Folio problem, the (Induced) Disjoint Paths problem, and the F-Minor-Deletion problem.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 12 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.