Fast approximation of steiner trees in large graphs

Andrey Gubichev, Thomas Neumann
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引用次数: 22

Abstract

Finding the minimum connected subtree of a graph that contains a given set of nodes (i.e., the Steiner tree problem) is a fundamental operation in keyword search in graphs, yet it is known to be NP-hard. Existing approximation techniques either make use of the heavy indexing of the graph, or entirely rely on online heuristics. In this paper we bridge the gap between these two extremes and present a scalable landmark-based index structure that, combined with a few lightweight online heuristics, yields a fast and accurate approximation of the Steiner tree. Our solution handles real-world graphs with millions of nodes and provides an approximation error of less than 5% on average.
大图中steiner树的快速逼近
寻找包含给定节点集的图的最小连通子树(即Steiner树问题)是图中关键字搜索的基本操作,但已知它是np困难的。现有的近似技术要么利用图的大量索引,要么完全依赖于在线启发式。在本文中,我们弥合了这两个极端之间的差距,并提出了一个可扩展的基于地标的索引结构,结合一些轻量级的在线启发式方法,产生了一个快速而准确的Steiner树近似。我们的解决方案处理具有数百万个节点的真实图形,并提供平均小于5%的近似误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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