分布式随机算法用于PageRank计算
Distributed Randomized Algorithms for the PageRank Computation
AI总结：
针对PageRank计算问题，提出了几种分布式随机算法，通过局部通信实现渐近均方收敛到真实PageRank值，并讨论了与多智能体一致性问题的紧密联系。
摘要：
在谷歌搜索引擎中，PageRank算法在搜索结果排名中起着关键作用。该算法基于网页的链接结构量化每个网页的重要性。我们首先概述了原始问题的设置。然后，我们提出了几种用于计算PageRank的分布式随机方案，其中页面可以通过与链接相连的页面进行通信来局部更新其值。本文的主要目的是证明这些方案在均方意义上渐近收敛到真实的PageRank值。还详细讨论了与多智能体一致性问题的紧密联系。
英文摘要：
In the search engine of Google, the PageRank algorithm plays a crucial role in ranking the search results. The algorithm quantifies the importance of each web page based on the link structure of the web. We first provide an overview of the original problem setup. Then, we propose several distributed randomized schemes for the computation of the PageRank, where the pages can locally update their values by communicating to those connected by links. The main objective of the paper is to show that these schemes asymptotically converge in the mean-square sense to the true PageRank values. A detailed discussion on the close relations to the multi-agent consensus problems is also given.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
