Depth-First Search Performance in a Random Digraph with Geometric Degree Distribution

P. Jacquet, S. Janson
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引用次数: 2

Abstract

We present an analysis of the depth-first search algorithm in a random digraph model with independent outdegrees having a geometric distribution. The results include asymptotic results for the depth profile of vertices, the height (maximum depth) and average depth, the number of trees in the forest, the size of the largest and second-largest trees, and the numbers of arcs of different types in the depth-first jungle. Most results are first order. For the height we show an asymptotic normal distribution. This analysis proposed by Donald Knuth in his next to appear volume of The Art of Computer Programming gives interesting insight in one of the most elegant and efficient algorithm for graph analysis due to Tarjan.
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几何度分布随机有向图的深度优先搜索性能
我们分析了具有几何分布的独立外度随机有向图模型中的深度优先搜索算法。结果包括顶点的深度轮廓、高度(最大深度)和平均深度、森林中树木的数量、最大和第二大树木的大小以及深度优先丛林中不同类型的弧的数量的渐近结果。大多数结果都是一阶的。对于高度,我们给出了一个渐近正态分布。Donald Knuth在他即将出版的《计算机程序设计艺术》一书中提出的这一分析,对图分析中最优雅、最有效的算法之一Tarjan给出了有趣的见解。
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