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https://dx.doi.org/10.48550/ar...
Article . 2025
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A polynomial algorithm to compute the boxicity and threshold dimension of complements of block graphs

Authors: Caoduro, Marco; Evans, Will; Gaede, Tao;

A polynomial algorithm to compute the boxicity and threshold dimension of complements of block graphs

Abstract

The boxicity of a graph $G$ is the minimum dimension $d$ that admits a representation of $G$ as the intersection graph of a family of axis-parallel boxes in $\mathbb{R}^d$. Computing boxicity is an NP-hard problem, and there are few known graph classes for which it can be computed in polynomial time. One such class is the class of block graphs. A block graph is a graph in which every maximal $2$-connected component is a clique. Since block graphs are known to have boxicity at most two, computing their boxicity amounts to the linear-time interval graph recognition problem. On the other hand, complements of block graphs have unbounded boxicity, yet we show that there is also a polynomial algorithm that computes the boxicity of complements of block graphs. An adaptation of our approach yields a polynomial algorithm for computing the threshold dimension of the complements of block graphs, which for general graphs is an NP-hard problem. Our method suggests a general technique that may show the tractability of similar problems on block-restricted graph classes.

14 pages, 6 figures, submitted to the 20th International Conference and Workshops on Algorithms and Computation

Keywords

05C62 (primary) 05C10, 05C85 (secondary), Combinatorics, FOS: Mathematics, Combinatorics (math.CO)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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