Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
Machine Learning
2019-12-10 v2 Applications
Methodology
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.
Keywords
Cite
@article{arxiv.1401.3813,
title = {Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability},
author = {Heather Patsolic and Sancar Adali and Joshua T. Vogelstein and Youngser Park and Carey E. Friebe and Gongkai Li and Vince Lyzinski},
journal= {arXiv preprint arXiv:1401.3813},
year = {2019}
}
Comments
26 pages, 7 figures. Updated content and added application of simultaneous matching for several time-steps for zebrafish connectomes