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On Tree Representations of Relations and Graphs: Symbolic Ultrametrics and Cograph Edge Decompositions

机译:关于关系和图形的树表示:符号超尺度   和Cograph边缘分解

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摘要

Tree representations of (sets of) symmetric binary relations, or equivalentlyedge-colored undirected graphs, are of central interest, e.g.\ inphylogenomics. In this context symbolic ultrametrics play a crucial role.Symbolic ultrametrics define an edge-colored complete graph that allows torepresent the topology of this graph as a vertex-colored tree. Here, we areinterested in the structure and the complexity of certain combinatorialproblems resulting from considerations based on symbolic ultrametrics, and onalgorithms to solve them. This includes, the characterization of symbolic ultrametrics thatadditionally distinguishes between edges and non-edges of \emph{arbitrary}edge-colored graphs $G$ and thus, yielding a tree representation of $G$, bymeans of so-called cographs. Moreover, we address the problem of finding"closest" symbolic ultrametrics and show the NP-completeness of the threeproblems: symbolic ultrametric editing, completion and deletion. Finally, asnot all graphs are cographs, and hence, don't have a tree representation, weask, furthermore, what is the minimum number of cotrees needed to represent thetopology of an arbitrary non-cograph $G$. This is equivalent to find an optimalcograph edge $k$-decomposition $\{E_1,\dots,E_k\}$ of $E$ so that each subgraph$(V,E_i)$ of $G$ is a cograph. We investigate this problem in full detail,resulting in several new open problems, and NP-hardness results. For all optimization problems proven to be NP-hard we will provide integerlinear program (ILP) formulations to efficiently solve them.
机译:对称二元关系(的集合)或等价的边缘色无向图的树表示非常重要,例如\系统发育组学。在这种情况下,符号超度量起着至关重要的作用。符号超度量定义了一个边色完整的图,该图允许将该图的拓扑表示为顶点色的树。在这里,我们对某些组合问题的结构和复杂性感兴趣,这些问题是基于符号超度量的考虑以及解决这些问题的算法而产生的。这包括对符号超计量学的表征,该特征可另外区分\ emph {任意}边色图$ G $的边缘和非边缘,从而产生所谓的Cograph的树形表示$ G $。此外,我们解决了找到“最接近的”符号超度量的问题,并显示了三个问题的NP完整性:符号超度量的编辑,完成和删除。最后,并非所有图都是cograph,因此没有树表示,因此,wesk表示任意非cograph $ G $的拓扑所需的cotree的最小数量是多少。这等效于找到$ E $的最优图形边$ k $-分解$ \ {E_1,\ dots,E_k \} $,以便$ G $的每个子图$(V,E_i)$是一个图形。我们对这个问题进行了详尽的调查,结果发现了几个新的未解决问题,并得出了NP硬度结果。对于被证明是NP困难的所有优化问题,我们将提供整数线性程序(ILP)公式来有效地解决它们。

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