Proceedings of the International Conference on Pattern Recognition Applications and Methods 2015
DOI: 10.5220/0005209202710278
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An Exact Graph Edit Distance Algorithm for Solving Pattern Recognition Problems

Abstract: International audienceGraph edit distance is an error tolerant matching technique emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning and data mining; it represents the minimum-cost sequence of basic edit operations to transform one graph into another by means of insertion, deletion and substitution of vertices and/or edges. A widely used method for exact graph edit distance computation is based on the A* algorithm. To … Show more

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Cited by 136 publications
(98 citation statements)
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“…The comparative analysis of the tested methods is performed by considering a graph edit distance (GED) [11] d(g t+1 ,ĝ t+1 )…”
Section: Methodsmentioning
confidence: 99%
“…The comparative analysis of the tested methods is performed by considering a graph edit distance (GED) [11] d(g t+1 ,ĝ t+1 )…”
Section: Methodsmentioning
confidence: 99%
“…To overcome the bottleneck of the best rst BnB algorithm referred to as A * in (Neuhaus et al, 2006a), a recent GED algorithm, referred to as DF, was put forward in (Abu-Aisheh et al, 2015) to reduce the memory consumption and also the computation time. This was done using a dierent exploration strategy (i.e., depth-rst instead of best-rst).…”
Section: The Graph Edit Distance Problemmentioning
confidence: 99%
“…Finding the exact kNN can be done through the calculation of an exact GED (such as in Abu-Aisheh et al (2015); Neuhaus et al (2006a)). This approach has an exponential complexity in function of the size of compared graphs and a linear complexity in function of the number of training graphs.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…At present, two main families of error-tolerant GM methods can be found in the literature: exact and approximate. Few exact methods have been found in the literature (Justice and Hero (2006); Riesen et al (2007); Abu-Aisheh et al (2015a)). On the other hand, a number of approximate GM methods have been proposed with reduced computational time and accuracy.…”
Section: Introductionmentioning
confidence: 99%