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We present a novel approximate graph matching algorithm that incorporatesseeded data into the graph matching paradigm. Our Joint Optimization ofFidelity and Commensurability (JOFC) algorithm embeds two graphs into a commonEuclidean space where the matching inference task can be performed. Throughreal and simulated data examples, we demonstrate the versatility of ouralgorithm in matching graphs with various characteristics--weightedness,directedness, loopiness, many-to-one and many-to-many matchings, and softseedings.

Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability