Graph Edit Distance: Moving from global to local structure to solve the graph-matching problem

Serratosa, Francesc; Cortés, Xavier

doi:10.1016/j.patrec.2015.08.003

Cited by 40 publications

(19 citation statements)

References 36 publications

(53 reference statements)

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“…To calculate a more accurate dissimilarity measure between two graph correspondences, we have considered the Star local substructure due to its trade-off between simplicity and robustness [38]. A Star within a graph is composed of a node, its connecting edges and incident nodes.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Moreno‐García

Serratosa

Jiang

2020

Pattern Recognition Letters

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Edit distance and graph matching Weighted mean and generalized median Definition of a distance between graph correspondences New algorithm to deduce the set of weighted means of correspondences

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Section: Accepted Manuscriptmentioning

confidence: 99%

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Moreno‐García

Serratosa

Jiang

2020

Pattern Recognition Letters

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“…These cost functions estimate the degree of dissimilarity between a pair of nodes v p i and v q j belonging to graphs G p and G q . The Euclidean distance is a common way to estimate the local distance between the nodes attributes, while in (Serratosa and Cortés, 2015) different metrics are presented to estimate the structural distance. In our framework, the idea is to automatically learn a model that estimates these costs, from a collection of training correspondences previously labeled, instead of having to define it manually.…”

Section: Edit Costs For Graphs Edit Distancementioning

confidence: 99%

Learning edit cost estimation models for graph edit distance

Cortés

Conte

Cardot

2019

Pattern Recognition Letters

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One of the most popular distance measures between a pair of graphs is the Graph Edit Distance. This approach consists of finding a set of edit operations that completely transforms a graph into another. Edit costs are introduced in order to penalize the distortion that each edit operation introduces. Then, one basic requirement when we design a Graph Edit Distance algorithm, is to define the appropriate edit cost functions. On the other hand, Machine Learning algorithms has been applied in many contexts showing impressive results, due, among other things, its ability to find correlations between input and output values. The aim of this paper is to bring the potentialities of Machine Learning to the Graph Edit Distance problem presenting a general framework in which this kind of algorithms are used to estimate the edit costs values for node substitutions.

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“….n} are functions that map the nodes and edges to their attribute values, respectively. ψ i ∈ R m maps each node to its m local attributes and E(•) refers to the degree of a certain node [14,15]. For simplicity, in this paper we only consider undirected and unattributed edges.…”

Section: Attributed Graphs and Graph Edit Distancementioning

confidence: 99%

“…Moreover, we compared our approach to four graph-matching methods (Table 1). In the first two methods, the cost function is defined as an edit cost based on the Euclidean distance between the node attributes plus the difference of the degree of these attributes, as it was done in [14,15]. In the other two, the cost function is defined as the output of a Neural Network previously trained as in [7].…”

Section: Graph Matching Performancementioning

confidence: 99%

Sub-optimal Graph Matching by Node-to-Node Assignment Classification

Cortés

Conte

Serratosa

2019

Graph-Based Representations in Pattern Recognition

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In the recent years, Graph Edit Distance has awaken interest in the scientific community and some new graph-matching algorithms that compute it have been presented. Nevertheless, these algorithms usually cannot be used in real applications due to runtime restrictions. For this reason, other graph-matching algorithms have also been used that compute an approximation of the graph correspondence with lower runtime. Clearly, in a real application, there is a tradeoff between runtime and accuracy. One of the most costly part in these algorithms is the deduction of the node-to-node mapping. We present a new graph-matching algorithm that returns a graph correspondence without the explicit computation of the assignment problem. This is done thanks to a classification of the node-to-node assignment learned in a previous training stage.

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Graph Edit Distance: Moving from global to local structure to solve the graph-matching problem

Cited by 40 publications

References 36 publications

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Correspondence edit distance to obtain a set of weighted means of graph correspondences

Learning edit cost estimation models for graph edit distance

Sub-optimal Graph Matching by Node-to-Node Assignment Classification

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