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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