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. Without involving the information from "aliens", Google's PageRank could not be well-defined. 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, and our conclusion is 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 PageRank 统计数据是谷歌、 Sergey Brin 和 Larry Page 的共同创始人于1998年创建的,目的是优化网站的排名,以取得搜索引擎的结果。它使用迭代算法计算,其依据的理念是,具有更多进场边缘的节点更为重要。谷歌的 PageRank 包含来自“ aliens” 的一些信息; 15%的信息被视为来自所考虑的网络系统外部的连接。 谷歌的 PageRank 不包含来自“ aliens ” 的信息, Google的 PageRank 无法很好地定义。 在本文中, 寻找稳定的数据“ 关闭 ” 至 Page Rank 的“ intrinsic” 版本。 我们将引入名为 MarkovRank 的新统计。 将特别关注标准- Page Rank ” 、 “ intrinsc- Page- Page- Rank ” 和 MarkovRank 之间等级统计的比较。 我们的结论是, 马科夫兰克的等级统计始终定义明确, 与“ ” 定义非常一致。