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arXiv 1508.04725cs.DM
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将图划分为诱导子图

Partitioning graphs into induced subgraphs

Dušan Knop

摘要:

本文从参数化复杂性的角度研究了诱导$H$划分问题。在诱导$H$划分问题中,任务是将图$G$的顶点划分为集合$V_1,V_2,\dots,V_n$,使得图$H$同构于由$i = 1,2,\dots,n.$的每个集合$V_i$诱导的$G$的子图。模式图$H$是固定的。对于参数化,我们考虑图$G$的三个不同的结构参数-即树宽度,邻域多样性和模宽度。对于邻域多样性参数化,我们得到了每个图$H.$的FPT算法;对于树宽参数化,我们得到了每个连通图$H.$的FPT算法;最后,对于模宽参数化,我们得到了每个素图$H.$的FPT算法

英文摘要:

We study the Induced $H$ Partition problem from the parameterized complexity point of view. In the Induced $H$ Partition problem the task is to partition vertices of a graph $G$ into sets $V_1,V_2,\dots,V_n$ such that the graph $H$ is isomorphic to the subgraph of $G$ induced by each set $V_i$ for $i = 1,2,\dots,n.$ The pattern graph $H$ is fixed. For the parametrization we consider three distinct structural parameters of the graph $G$ - namely the tree-width, the neighborhood diversity, and the modular-width. For the parametrization by the neighborhood diversity we obtain an FPT algorithm for every graph $H.$ For the parametrization by the tree-width we obtain an FPT algorithm for every connected graph $H.$ Finally, for the parametrization by the modular-width we derive an FPT algorithm for every prime graph $H.$

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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