scholarly journals Solving the Graph Edit Distance Problem with Variable Partitioning Local Search

2019 ◽  
pp. 67-77
Author(s):  
Mostafa Darwiche ◽  
Donatello Conte ◽  
Romain Raveaux ◽  
Vincent T’kindt
Keyword(s):  
Local Search ◽  
Edit Distance ◽  
Graph Edit Distance ◽  
Distance Problem

scholarly journals A local branching heuristic for solving a Graph Edit Distance Problem

2019 ◽  
Vol 106 ◽  
pp. 225-235 ◽  
Author(s):  
Mostafa Darwiche ◽  
Donatello Conte ◽  
Romain Raveaux ◽  
Vincent T’Kindt
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
Local Branching ◽  
Distance Problem

scholarly journals Improved local search for graph edit distance

2020 ◽  
Vol 129 ◽  
pp. 19-25 ◽  
Author(s):  
Nicolas Boria ◽  
David B. Blumenthal ◽  
Sébastien Bougleux ◽  
Luc Brun
Keyword(s):  
Local Search ◽  
Edit Distance ◽  
Graph Edit Distance

On-line learning the graph edit distance costs

2021 ◽  
Author(s):  
Elena Rica ◽  
Susana Álvarez ◽  
Francesc Serratosa
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
On Line ◽  
On Line Learning

scholarly journals An efficient algorithm for graph edit distance computation

2019 ◽  
Vol 163 ◽  
pp. 762-775 ◽  
Author(s):  
Xiaoyang Chen ◽  
Hongwei Huo ◽  
Jun Huan ◽  
Jeffrey Scott Vitter
Keyword(s):  
Efficient Algorithm ◽  
Edit Distance ◽  
Graph Edit Distance ◽  
Distance Computation

scholarly journals Redefining the Graph Edit Distance

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Francesc Serratosa
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Triangle Inequality ◽  
Edit Distance ◽  
Minimum Amount ◽  
Graph Edit Distance ◽  
Edit Operation ◽  
Attributed Graphs ◽  
Distance Properties

AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs. This distance is defined as the minimum amount of edit operations (deletion, insertion and substitution of nodes and edges) needed to transform a graph into another. Since now, it has been stated that the distance properties have to be applied [(1) non-negativity (2) symmetry (3) identity and (4) triangle inequality] to the involved edit operations in the process of computing the graph edit distance to make the graph edit distance a metric. In this paper, we show that there is no need to impose the triangle inequality in each edit operation. This is an important finding since in pattern recognition applications, the classification ratio usually maximizes in the edit operation combinations (deletion, insertion and substitution of nodes and edges) that the triangle inequality is not fulfilled.

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