Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability

Abstract

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.