对于常数k,在多项式时间内识别k次幂

IF 0.9 3区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Manuel Lafond
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引用次数: 4

摘要

如果存在树T,其叶集为V(G),并且当且仅当T中u和V之间的距离不大于k(且u≠V)时,则图G是k叶幂。k叶幂的图类在计算生物学中有许多应用,但在过去的二十年中,识别它们仍然是一个具有挑战性的算法问题。最著名的结果是6叶幂可以在多项式时间内被识别。在本文中,我们提出了一种算法来判断图G是否是一个k叶幂在时间O(nf(k))对于某个函数f,它只依赖于k(但具有功率塔函数的增长率)。我们的技术是基于这样的事实:要么一个k叶幂有一个对应的最大度低的树,在这种情况下很容易找到它,要么每个对应的树都有一个大的最大度。在后一种情况下,树中的大度顶点意味着G具有冗余的子结构,可以从图中修剪。除了解决一个长期存在的开放问题外,我们希望本工作中提出的结构结果可以导致k叶幂和相关类的进一步结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognizing k-leaf powers in polynomial time, for constant k
A graph G is a k-leaf power if there exists a tree T whose leaf set is V(G), and such that uv ∈ E(G) if and only if the distance between u and v in T is at most k (and u ≠ v). The graph classes of k-leaf powers have several applications in computational biology, but recognizing them has remained a challenging algorithmic problem for the past two decades. The best known result is that 6-leaf powers can be recognized in polynomial time. In this paper, we present an algorithm that decides whether a graph G is a k-leaf power in time O(nf(k)) for some function f that depends only on k (but has the growth rate of a power tower function). Our techniques are based on the fact that either a k-leaf power has a corresponding tree of low maximum degree, in which case finding it is easy, or every corresponding tree has large maximum degree. In the latter case, large degree vertices in the tree imply that G has redundant substructures which can be pruned from the graph. In addition to solving a longstanding open problem, we hope that the structural results presented in this work can lead to further results on k-leaf powers and related classes.
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来源期刊
ACM Transactions on Algorithms
ACM Transactions on Algorithms COMPUTER SCIENCE, THEORY & METHODS-MATHEMATICS, APPLIED
CiteScore
3.30
自引率
0.00%
发文量
50
审稿时长
6-12 weeks
期刊介绍: ACM Transactions on Algorithms welcomes submissions of original research of the highest quality dealing with algorithms that are inherently discrete and finite, and having mathematical content in a natural way, either in the objective or in the analysis. Most welcome are new algorithms and data structures, new and improved analyses, and complexity results. Specific areas of computation covered by the journal include combinatorial searches and objects; counting; discrete optimization and approximation; randomization and quantum computation; parallel and distributed computation; algorithms for graphs, geometry, arithmetic, number theory, strings; on-line analysis; cryptography; coding; data compression; learning algorithms; methods of algorithmic analysis; discrete algorithms for application areas such as biology, economics, game theory, communication, computer systems and architecture, hardware design, scientific computing
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