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Search in Power-Law Node Degree Networks

机译:在幂律节点学位网络中搜索

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Graphs in which decentralized algorithms can find short paths efficiently by considering only local information are known as Searchable Networks [4], [5]. Jon Kleinberg studied decentralized search in a subtle variant of the Watts-Strogatz model [3] and showed that the structure of searchable networks has to meet some strict requirements [5]. The aim of this paper is to investigate how it would be decentralized search in small- world models which combine wide variation in node degrees with some kind of spatial embedding. Through simulations, we evaluate the performance of a decentralized greedy algorithm and compare its efficiency versus Kleinberg's results. Our results indicates that networks with wide variation in node degrees can allow decentralized algorithms to achieve better average delivery times than the ones obtained by Kleinberg in [4].
机译:其中分散算法可以通过考虑只考虑本地信息有效地找到短路的图表是可搜索的网络[4],[5]。 Jon Kleinberg在瓦特 - Strogatz模型的微妙变种中研究了分散的搜索[3]并显示可搜索网络的结构必须满足一些严格的要求[5]。 本文的目的是调查如何在小型世界模型中进行分散搜索,这与某种空间嵌入的节点学位相结合了广泛的变化。 通过模拟,我们评估分散的贪婪算法的性能,并比较其效率与Kleinberg的结果。 我们的结果表明,节点度宽变化的网络可以允许分散的算法来实现比通过Kleinberg所获得的更好的平均交付时间来实现[4]。

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