使用自然切割的图划分

D. Delling, A. Goldberg, Ilya P. Razenshteyn, Renato F. Werneck
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引用次数: 130

摘要

我们提出了一种基于\emph{自然切割}概念的图划分新方法。我们的算法,叫做PUNCH,有两个阶段。第一阶段执行一系列的最小切割计算来识别和收缩图的密集区域。这减少了图的大小,但保留了它的一般结构。第二阶段使用贪婪和局部搜索启发式的组合来组装最终的分区。该算法在道路网络上表现得特别好,因为道路网络有大量的自然切口(如桥梁、山口和渡轮)。在几分钟内,它就获得了大陆大小网络的最著名的分区,大大改进了以前的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph Partitioning with Natural Cuts
We present a novel approach to graph partitioning based on the notion of \emph{natural cuts}. Our algorithm, called PUNCH, has two phases. The first phase performs a series of minimum-cut computations to identify and contract dense regions of the graph. This reduces the graph size, but preserves its general structure. The second phase uses a combination of greedy and local search heuristics to assemble the final partition. The algorithm performs especially well on road networks, which have an abundance of natural cuts (such as bridges, mountain passes, and ferries). In a few minutes, it obtains the best known partitions for continental-sized networks, significantly improving on previous results.
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