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Towards Controllability Analysis of Dynamic Networks Using Minimum Dominating Set

机译：使用最小支配集的动态网络可控性分析

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Finding a minimum dominating set is a classic NP-hard problem from graph theory. Given a finite, simple, undirected graph, it seeks a smallest set of vertices with the property that every vertex in the graph is either in or adjacent to at least one member of that set. In recent years, it has found increased application, particularly when used as the basis for classifying nodes of biological networks. Sample networks include those derived from metabolic, noncoding RNA and protein-protein interaction data. Classification schemes employed to date, however, have typically been limited by the need to solve multiple problem instances, which naturally constrains the size of amenable networks. Moreover, analytical methods based on minimum dominating set have thus far generally been limited to static graphs. In this paper, work in progress is described that improves upon these algorithms and applies them to dynamic streaming graphs in order to capture control structures as they evolve over time. Results demonstrate the effectiveness of these techniques at reducing computational overhead. A systematic experimental setup and a description of testbed construction is also provided.
机译：根据图论，找到一个最小的控制集是一个典型的NP难题。给定一个有限的，简单的，无向的图，它会寻找具有这些图的每个顶点都在该图的至少一个成员之中或与其相邻的属性的最小顶点集合。近年来，发现了越来越多的应用，特别是当用作对生物网络的节点进行分类的基础时。样本网络包括那些来自代谢，非编码RNA和蛋白质-蛋白质相互作用数据的网络。然而，迄今为止采用的分类方案通常受到解决多个问题实例的需求的限制，这自然限制了可适应网络的规模。而且，到目前为止，基于最小支配集的分析方法通常仅限于静态图。在本文中，我们描述了正在进行的工作，这些工作改进了这些算法，并将其应用于动态流图，以便捕获随着时间变化的控制结构。结果证明了这些技术在减少计算开销方面的有效性。还提供了系统的实验设置以及对试验台结构的描述。

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