Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
This work addresses graph matching challenges for researchers and practitioners in network analysis, though it appears incremental as it builds on existing graph matching paradigms.
The authors tackled the problem of graph matching by developing a novel algorithm that incorporates seeded data, demonstrating its versatility across various graph characteristics such as weightedness and directedness.
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.