  Seeded PageRank is an important network analysis tool for identifying and
studying regions nearby a given set of nodes, which are called seeds. The
seeded PageRank vector is the stationary distribution of a random walk that
randomly resets at the seed nodes. Intuitively, this vector is concentrated
nearby the given seeds, but is mathematically non-zero for all nodes in a
connected graph. We study this concentration, or localization, and show a
sublinear upper bound on the number of entries required to approximate seeded
PageRank on all graphs with a natural type of skewed-degree sequence---similar
to those that arise in many real-world networks. Experiments with both
real-world and synthetic graphs give further evidence to the idea that the
degree sequence of a graph has a major influence on the localization behavior
of seeded PageRank. Moreover, we establish that this localization is
non-trivial by showing that complete-bipartite graphs produce seeded PageRank
vectors that cannot be approximated with a sublinear number of non-zeros.
