English

Characterizing and Recognizing Twistedness

Computational Geometry 2025-08-25 v1 Combinatorics

Abstract

In a simple drawing of a graph, any two edges intersect in at most one point (either a common endpoint or a proper crossing). A simple drawing is generalized twisted if it fulfills certain rather specific constraints on how the edges are drawn. An abstract rotation system of a graph assigns to each vertex a cyclic order of its incident edges. A realizable rotation system is one that admits a simple drawing such that at each vertex, the edges emanate in that cyclic order, and a generalized twisted rotation system can be realized as a generalized twisted drawing. Generalized twisted drawings have initially been introduced to obtain improved bounds on the size of plane substructures in any simple drawing of KnK_n. They have since gained independent interest due to their surprising properties. However, the definition of generalized twisted drawings is very geometric and drawing-specific. In this paper, we develop characterizations of generalized twisted drawings that enable a purely combinatorial view on these drawings and lead to efficient recognition algorithms. Concretely, we show that for any n7n \geq 7, an abstract rotation system of KnK_n is generalized twisted if and only if all subrotation systems induced by five vertices are generalized twisted. This implies a drawing-independent and concise characterization of generalized twistedness. Besides, the result yields a simple O(n5)O(n^5)-time algorithm to decide whether an abstract rotation system is generalized twisted and sheds new light on the structural features of simple drawings. We further develop a characterization via the rotations of a pair of vertices in a drawing, which we then use to derive an O(n2)O(n^2)-time algorithm to decide whether a realizable rotation system is generalized twisted.

Keywords

Cite

@article{arxiv.2508.16178,
  title  = {Characterizing and Recognizing Twistedness},
  author = {Oswin Aichholzer and Alfredo García and Javier Tejel and Birgit Vogtenhuber and Alexandra Weinberger},
  journal= {arXiv preprint arXiv:2508.16178},
  year   = {2025}
}

Comments

Appears in the proceedings of the 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)