Spectral Clustering for Divide-and-Conquer Graph Matching
Machine Learning
2015-03-13 v5 Optimization and Control
Computation
Abstract
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic block model random graphs are correctly matched utilizing very few seeds through our divide-and-conquer procedure. We also demonstrate the effectiveness of our approach in matching very large graphs in simulated and real data examples, showing up to a factor of 8 improvement in runtime with minimal sacrifice in accuracy.
Cite
@article{arxiv.1310.1297,
title = {Spectral Clustering for Divide-and-Conquer Graph Matching},
author = {Vince Lyzinski and Daniel L. Sussman and Donniell E. Fishkind and Henry Pao and Li Chen and Joshua T. Vogelstein and Youngser Park and Carey E. Priebe},
journal= {arXiv preprint arXiv:1310.1297},
year = {2015}
}
Comments
32 pages, 8 figures