Spectral clustering for divide-and-conquer graph matching

•We present a novel divide-and-conquer bijective graph matching algorithm.•The algorithm is fully parallelizable, and scales to match “big data” graphs.•We demonstrate the effectiveness of the algorithm by matching DTMRI human connectomes. We present a parallelized bijective graph matching algorithm...

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Bibliographic Details
Published in:Parallel computing Vol. 47; pp. 70 - 87
Main Authors: Lyzinski, Vince, Sussman, Daniel L., Fishkind, Donniell E., Pao, Henry, Chen, Li, Vogelstein, Joshua T., Park, Youngser, Priebe, Carey E.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2015
Subjects:
Adjacency spectral embedding
Clustering
Graph matching
Stochastic block model
Adjacency spectral embedding
Clustering
Stochastic block model
Graph matching
ISSN:0167-8191, 1872-7336
Online Access:Get full text
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Summary:•We present a novel divide-and-conquer bijective graph matching algorithm.•The algorithm is fully parallelizable, and scales to match “big data” graphs.•We demonstrate the effectiveness of the algorithm by matching DTMRI human connectomes. 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.
ISSN:0167-8191
1872-7336
DOI:10.1016/j.parco.2015.03.004