  On the Web, visits of a page are often introduced by one or more valuable
linking sources. Indeed, good back links are valuable resources for Web pages
and sites. We propose to discovering and leveraging the best backlinks of pages
for ranking. Similar to PageRank, MaxRank scores are updated {recursively}. In
particular, with probability $\lambda$, the MaxRank of a document is updated
from the backlink source with the maximum score; with probability $1-\lambda$,
the MaxRank of a document is updated from a random backlink source. MaxRank has
an interesting relation to PageRank. When $\lambda=0$, MaxRank reduces to
PageRank; when $\lambda=1$, MaxRank only looks at the best backlink it thinks.
Empirical results on Wikipedia shows that the global authorities are very
influential; Overall large $\lambda$s (but smaller than 1) perform best: the
convergence is dramatically faster than PageRank, but the performance is still
comparable. We study the influence of these sources and propose a few measures
such as the times of being the best backlink for others, and related properties
of the proposed algorithm. The introduction of best backlink sources provides
new insights for link analysis. Besides ranking, our method can be used to
discover the most valuable linking sources for a page or Website, which is
useful for both search engines and site owners.
