  We consider the problem of optimal recovery of true ranking of $n$ items from
a randomly chosen subset of their pairwise preferences. It is well known that
without any further assumption, one requires a sample size of $\Omega(n^2)$ for
the purpose. We analyze the problem with an additional structure of relational
graph $G([n],E)$ over the $n$ items added with an assumption of
\emph{locality}: Neighboring items are similar in their rankings. Noting the
preferential nature of the data, we choose to embed not the graph, but, its
\emph{strong product} to capture the pairwise node relationships. Furthermore,
unlike existing literature that uses Laplacian embedding for graph based
learning problems, we use a richer class of graph
embeddings---\emph{orthonormal representations}---that includes (normalized)
Laplacian as its special case. Our proposed algorithm, {\it Pref-Rank},
predicts the underlying ranking using an SVM based approach over the chosen
embedding of the product graph, and is the first to provide \emph{statistical
consistency} on two ranking losses: \emph{Kendall's tau} and \emph{Spearman's
footrule}, with a required sample complexity of $O(n^2
\chi(\bar{G}))^{\frac{2}{3}}$ pairs, $\chi(\bar{G})$ being the \emph{chromatic
number} of the complement graph $\bar{G}$. Clearly, our sample complexity is
smaller for dense graphs, with $\chi(\bar G)$ characterizing the degree of node
connectivity, which is also intuitive due to the locality assumption e.g.
$O(n^\frac{4}{3})$ for union of $k$-cliques, or $O(n^\frac{5}{3})$ for random
and power law graphs etc.---a quantity much smaller than the fundamental limit
of $\Omega(n^2)$ for large $n$. This, for the first time, relates ranking
complexity to structural properties of the graph. We also report experimental
evaluations on different synthetic and real datasets, where our algorithm is
shown to outperform the state-of-the-art methods.
