On rank statistics of PageRank and MarkovRank
Yoichi Nishiyama

TL;DR
This paper introduces MarkovRank, a new stable rank statistic closely related to intrinsic PageRank, and compares it with standard PageRank and intrinsic PageRank, highlighting their equivalence under certain conditions.
Contribution
The paper proposes MarkovRank as a stable alternative to PageRank and demonstrates its equivalence to intrinsic PageRank when both are well-defined.
Findings
MarkovRank is always well-defined.
MarkovRank's rank statistic matches intrinsic PageRank.
Comparison shows equivalence under certain conditions.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWeb Data Mining and Analysis · Complex Network Analysis Techniques · Data Management and Algorithms
