The graph crossing number of a "good" graph (i.e., one for which all intersectinggraph edges intersect in a single point and arise from
four distinct graph vertices) is the minimum possible
number of crossings with which the graph can be drawn,
including using curved (non-rectilinear) graph edges.
Several notational conventions exist in the literature, with some of the more common
being
(e.g., Pan and Richter 2007; Clancy et al. 2020), (Pach and Tóth 2000b), (Pach and Tóth 2005), and .
A graph with crossing number 0 is known as a planar graph. There appears to be no term in standard use for a graph
with graph crossing number 1. In particular, the terms "almost
planar graph" and "1-planar graph"
are used in the literature for other concepts. Therefore, in this work, the term
singlecross graph will be used for a graph
with crossing number 1. A graph with crossing number 2
is known as a doublecross graph. These terms
are summarized in the table below.
Garey and Johnson (1983ab) showed that determining the crossing number is an NP-complete problem. Buchheim et al. (2008) used integer
programming to formulate the first exact algorithm
to find provably optimal crossing numbers. Chimani et al. (2008) subsequently
gave an integer programming formulation based
on Kuratowski constraints that can be practically
efficient for instances with crossing number up to 37. Their implementation computes
a crossing number using branch-and-cut-and-price based on Buchheim et al. (2008),
Chimani et al. (2008), and related work by the authors. The authors provide
a web form requesting application of this algorithm
to submitted graphs (Chimani and Wiedera). In contrast,
Haythorpe and colleagues implemented a fast heuristic
known as QuickCross which is designed to find optimal or near-optimal graph
embeddings, as discussed by Clancy et al. (2018).
Ajtai et al. (1982) showed that there is an absolute constant such that
for every graph with vertex count
and edge count. Ajtai et al. (1982) established that the inequality holds for , and subsequently improved to 1/64 (cf. Clancy et
al. 2020).
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