Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability

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.