The well-known statistic PageRank was created in 1998 by co-founders of Google, Sergey Brin and Larry Page, to optimize the ranking of websites for their search engine outcomes. It is computed using an iterative algorithm, based on the idea that nodes with a larger number of incoming edges are more important. Google's PageRank involves some information from ``aliens''; the 15% of information is regarded as the connections from the outside of the network system under consideration. In this paper, seeking a stable statistic which is ``close'' to an ``intrinsic'' version of PageRank, we will introduce a new statistic called MarkovRank. A special attention will be paid to the comparison of rank statistics among standard-PageRank,``intrinsic-PageRank'' and MarkovRank. It is concluded that the rank statistic of MarkovRank, which is always well-defined, is identical to that of ``intrinsic-PageRank'', as far as the latter is well-defined.

翻译：众所周知的统计量 PageRank 由谷歌联合创始人谢尔盖·布林和拉里·佩奇于 1998 年提出，旨在优化搜索引擎结果的网站排名。该统计量基于“入边数量越多的节点越重要”这一思想，通过迭代算法计算得出。谷歌的 PageRank 包含来自“外部节点”的某些信息：其中 15% 的信息被视为所考虑网络系统外部的连接。本文中，为寻求一种稳定且“接近”PageRank“固有”版本的统计量，我们将引入一种名为 MarkovRank 的新统计量。我们特别关注标准 PageRank、“固有 PageRank”与 MarkovRank 之间排序统计量的比较。结论表明：始终良定义的 MarkovRank 的排序统计量，与“固有 PageRank”（在其良定义的前提下）的排序统计量完全相同。