  We consider the problem of learning the qualities of a collection of items by
performing noisy comparisons among them. Following the standard paradigm, we
assume there is a fixed "comparison graph" and every neighboring pair of items
in this graph is compared $k$ times according to the Bradley-Terry-Luce model
(where the probability than an item wins a comparison is proportional the item
quality). We are interested in how the relative error in quality estimation
scales with the comparison graph in the regime where $k$ is large. We prove
that, after a known transition period, the relevant graph-theoretic quantity is
the square root of the resistance of the comparison graph. Specifically, we
provide an algorithm that is minimax optimal. The algorithm has a relative
error decay that scales with the square root of the graph resistance, and
provide a matching lower bound (up to log factors). The performance guarantee
of our algorithm, both in terms of the graph and the skewness of the item
quality distribution, outperforms earlier results.
