Perfect and nearly perfect separation dimension of complete and random graphs
Raphael Yuster

TL;DR
This paper investigates the perfect separation dimension of complete and random graphs, establishing it is linear in the number of vertices, and explores how relaxing perfection affects the dimension.
Contribution
It provides the first near-tight bounds for the perfect separation dimension of complete graphs and extends results to random graphs, revealing its linear growth and the impact of relaxation.
Findings
Perfect separation dimension of $K_n$ is between $n/2-1$ and $n( ext{log } n)^{1+o(1)}$.
Almost all graphs have a perfect separation dimension linear in $n$, up to a logarithmic factor.
Relaxing the perfection condition still requires the dimension to be linear in $n$, despite allowing some variation.
Abstract
The separation dimension of a hypergraph is the smallest natural number for which there is an embedding of into , such that any pair of disjoint edges is separated by some hyperplane normal to one of the axes. The perfect separation dimension further requires that any pair of disjoint edges is separated by the same amount of such (pairwise nonparallel) hyperplanes. While it is known that for any fixed , the separation dimension of any -vertex -graph is , the perfect separation dimension is much larger. In fact, no polynomial upper-bound for the perfect separation dimension of -uniform hypergraphs is known. In our first result we essentially resolve the case , i.e. graphs. We prove that the perfect separation dimension of is linear in , up to a small polylogarithmic factor. In fact, we prove it is at least and…
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