Partitioning the Bags of a Tree Decomposition Into Cliques

Thomas Bläsius, Maximilian Katzmann, Marcus Wilhelm
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Abstract

We consider a variant of treewidth that we call clique-partitioned treewidth in which each bag is partitioned into cliques. This is motivated by the recent development of FPT-algorithms based on similar parameters for various problems. With this paper, we take a first step towards computing clique-partitioned tree decompositions. Our focus lies on the subproblem of computing clique partitions, i.e., for each bag of a given tree decomposition, we compute an optimal partition of the induced subgraph into cliques. The goal here is to minimize the product of the clique sizes (plus 1). We show that this problem is NP-hard. We also describe four heuristic approaches as well as an exact branch-and-bound algorithm. Our evaluation shows that the branch-and-bound solver is sufficiently efficient to serve as a good baseline. Moreover, our heuristics yield solutions close to the optimum. As a bonus, our algorithms allow us to compute first upper bounds for the clique-partitioned treewidth of real-world networks. A comparison to traditional treewidth indicates that clique-partitioned treewidth is a promising parameter for graphs with high clustering.
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将树分解的包划分为小块
我们考虑树宽的一种变体,我们称之为派系划分树宽,其中每个包被划分为派系。这是由最近基于类似参数的各种问题的fpt算法的发展所激发的。在本文中,我们向计算团划分树分解迈出了第一步。我们的重点在于计算团分区的子问题,即对于给定树分解的每个包,我们计算诱导子图的最优分区到团。这里的目标是最小化团大小的乘积(加1)。我们证明这个问题是np困难的。我们还描述了四种启发式方法以及一种精确的分支定界算法。我们的评估表明,分支定界求解器是足够有效的,可以作为一个很好的基线。此外,我们的启发式方法产生了接近最优的解决方案。作为奖励,我们的算法允许我们计算现实世界网络的团划分树宽的第一个上界。与传统树宽的比较表明,对于高聚类的图,团划分树宽是一个很有前途的参数。
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