谷歌的PageRank算法，用于在一般网络中对节点进行排名

2016 13th International Workshop on Discrete Event Systems (WODES) Pub Date : 2016-05-01 DOI:10.1109/WODES.2016.7497841

J. Berkhout

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引用次数: 11

摘要

本文将Google的PageRank算法中的随机冲浪方法推广到一般有限马尔可夫链，这种有限马尔可夫链可以由多个遍历类和可能的瞬态组成。我们将引入马尔可夫链的扩展遍历投影的新概念，我们将展示扩展遍历投影如何允许直观的更好的暂态排序。数值算例说明了这种新的排序方法的效果。

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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Google's PageRank algorithm for ranking nodes in general networks

This paper extends the random surfer approach of Google's PageRank algorithm to general finite Markov chains that may consist of several ergodic classes and possible transient states. We will introduce the new concept of an extended ergodic projector of a Markov chain and we will show how the extended ergodic projector allows for intuitive better ranking of transient states. Numerical examples are provided to illustrate the effect of this new ranking approach.

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来源期刊

2016 13th International Workshop on Discrete Event Systems (WODES)

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