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The complexity of determining the rainbow vertex-connection of a graph

Authors: Lily Chen; Xueliang Li 0001; Yongtang Shi;

The complexity of determining the rainbow vertex-connection of a graph

Abstract

A vertex-colored graph is {\it rainbow vertex-connected} if any two vertices are connected by a path whose internal vertices have distinct colors, which was introduced by Krivelevich and Yuster. The {\it rainbow vertex-connection} of a connected graph $G$, denoted by $rvc(G)$, is the smallest number of colors that are needed in order to make $G$ rainbow vertex-connected. In this paper, we study the computational complexity of vertex-rainbow connection of graphs and prove that computing $rvc(G)$ is NP-Hard. Moreover, we show that it is already NP-Complete to decide whether $rvc(G)=2$. We also prove that the following problem is NP-Complete: given a vertex-colored graph $G$, check whether the given coloring makes $G$ rainbow vertex-connected.

7 pages

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Keywords

FOS: Computer and information sciences, computational complexity, Discrete Mathematics (cs.DM), Theoretical Computer Science, Computational complexity, Rainbow vertex-connection, Coloring of graphs and hypergraphs, Graph theory (including graph drawing) in computer science, FOS: Mathematics, Coloring, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), Mathematics - Combinatorics, Combinatorics (math.CO), rainbow vertex-connection, 05C15, 05C40, 68Q25, 68R10, coloring, Computer Science(all), Computer Science - Discrete Mathematics

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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!
33
Top 10%
Top 10%
Top 10%
Green
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