Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance

Vincenzo Carletti 1, 2 Benoit Gaüzère 1 Luc Brun 2 Mario Vento 1
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Graph edit distance corresponds to a flexible graph dissim-ilarity measure. Unfortunately, its computation requires an exponential complexity according to the number of nodes of both graphs being compared. Some heuristics based on bipartite assignment algorithms have been proposed in order to approximate the graph edit distance. However , these heuristics lack of accuracy since they are based either on small patterns providing a too local information or walks whose tottering induce some bias in the edit distance calculus. In this work, we propose to extend previous heuristics by considering both less local and more accurate patterns defined as subgraphs defined around each node.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [11 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01389626
Contributor : Luc Brun <>
Submitted on : Friday, October 28, 2016 - 5:48:22 PM
Last modification on : Thursday, February 7, 2019 - 4:53:26 PM

File

Approximate_Graph_Edit_Distanc...
Files produced by the author(s)

Identifiers

Citation

Vincenzo Carletti, Benoit Gaüzère, Luc Brun, Mario Vento. Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance. Graph-Based Representation in Pattern Recognition, Cheng-Lin Liu ; Bin Luo; Walter G. Kropatsch; Jian Cheng, May 2015, Bejing, China. pp.188 - 197, ⟨10.1007/978-3-319-18224-7_19⟩. ⟨hal-01389626⟩

Share

Metrics

Record views

141

Files downloads

339