Estimating degree distributions of large networks using non-backtracking random walk with non-uniform jump

Sirinda Palahan
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引用次数: 1

Abstract

This work presents a hybrid sampling method that mixes a non-backtracking random walk and a variation of random walk with jump. We show that the proposed method combines the strengths of both random walks. In particular, the walker of our method will not backtrack to the previously visited vertex so it is likely to produce less number of duplicate samples than the simple random walk. Moreover, the walker's ability to jump ensures that it will explore a network faster. We applied our method on six real world online networks where some of the networks contain millions of vertices. The experimental results show that our method outperformed a non-backtracking random walk and a random walk with jump on estimating degree distributions.
基于非均匀跳变的非回溯随机漫步估计大型网络的度分布
本文提出了一种混合非回溯随机行走和带跳跃的随机行走变体的混合抽样方法。我们表明,所提出的方法结合了两种随机漫步的优势。特别是,我们的方法的步行者不会回溯到之前访问的顶点,所以它可能比简单的随机漫步产生更少的重复样本。此外,步行者的跳跃能力确保了它能更快地探索网络。我们将我们的方法应用于六个真实的在线网络,其中一些网络包含数百万个顶点。实验结果表明,该方法在估计度分布上优于非回溯随机漫步和带跳跃随机漫步。
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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