  We present a novel approximate graph matching algorithm that incorporates
seeded data into the graph matching paradigm. Our Joint Optimization of
Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common
Euclidean space where the matching inference task can be performed. Through
real and simulated data examples, we demonstrate the versatility of our
algorithm in matching graphs with various characteristics--weightedness,
directedness, loopiness, many-to-one and many-to-many matchings, and soft
seedings.
