Paper detail

On the Complexity of Making a Distinguished Vertex Minimum or Maximum Degree by Vertex Deletion

In this paper, we investigate the approximability of two node deletion problems. Given a vertex weighted graph $G=(V,E)$ and a specified, or "distinguished" vertex $p \in V$, MDD(min) is the problem of finding a minimum weight vertex set $S \subseteq V\setminus \{p\}$ such that $p$ becomes the minimum degree vertex in $G[V \setminus S]$; and MDD(max) is the problem of finding a minimum weight vertex set $S \subseteq V\setminus \{p\}$ such that $p$ becomes the maximum degree vertex in $G[V \setminus S]$. These are known $NP$-complete problems and have been studied from the parameterized complexity point of view in previous work. Here, we prove that for any $ε> 0$, both the problems cannot be approximated within a factor $(1 - ε)\log n$, unless $NP \subseteq DTIME(n^{\log\log n})$. We also show that for any $ε> 0$, MDD(min) cannot be approximated within a factor $(1 -ε)\log n$ on bipartite graphs, unless $NP \subseteq DTIME(n^{\log\log n})$, and that for any $ε> 0$, MDD(max) cannot be approximated within a factor $(1/2 - ε)\log n$ on bipartite graphs, unless $NP \subseteq DTIME(n^{\log\log n})$. We give an $O(\log n)$ factor approximation algorithm for MDD(max) on general graphs, provided the degree of $p$ is $O(\log n)$. We then show that if the degree of $p$ is $n-O(\log n)$, a similar result holds for MDD(min). We prove that MDD(max) is $APX$-complete on 3-regular unweighted graphs and provide an approximation algorithm with ratio $1.583$ when $G$ is a 3-regular unweighted graph. In addition, we show that MDD(min) can be solved in polynomial time when $G$ is a regular graph of constant degree.

preprint2014arXivOpen access

Signal facts

What is known right now

Open access3 authors3 topics

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.