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.
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Communication dans un congrès
Cheng-Lin Liu ; Bin Luo; Walter G. Kropatsch; Jian Cheng. Graph-Based Representation in Pattern Recognition, May 2015, Bejing, China. Springer, Lecture notes in Computer Sciences, pp.188 - 197, 2015, Graph-Based Representation in Pattern Recognition. 〈http://www.nlpr.ia.ac.cn/gbr2015/index.html〉. 〈10.1007/978-3-319-18224-7_19〉
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Dernière modification le : jeudi 7 février 2019 - 16:53:26

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Vincenzo Carletti, Benoit Gaüzère, Luc Brun, Mario Vento. Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance. Cheng-Lin Liu ; Bin Luo; Walter G. Kropatsch; Jian Cheng. Graph-Based Representation in Pattern Recognition, May 2015, Bejing, China. Springer, Lecture notes in Computer Sciences, pp.188 - 197, 2015, Graph-Based Representation in Pattern Recognition. 〈http://www.nlpr.ia.ac.cn/gbr2015/index.html〉. 〈10.1007/978-3-319-18224-7_19〉. 〈hal-01389626〉

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