  Consider two networks on overlapping, non-identical vertex sets. Given
vertices of interest in the first network, we seek to identify the
corresponding vertices, if any exist, in the second network. While in
moderately sized networks graph matching methods can be applied directly to
recover the missing correspondences, herein we present a principled methodology
appropriate for situations in which the networks are too large for brute-force
graph matching. Our methodology identifies vertices in a local neighborhood of
the vertices of interest in the first network that have verifiable
corresponding vertices in the second network. Leveraging these known
correspondences, referred to as seeds, we match the induced subgraphs in each
network generated by the neighborhoods of these verified seeds, and rank the
vertices of the second network in terms of the most likely matches to the
original vertices of interest. We demonstrate the applicability of our
methodology through simulations and real data examples.
