URV's Author/s:  Serratosa Casanelles, Francesc d'Assís

Author, as appears in the article.:  Santacruz, Pep; Serratosa, Francesc;

Author's mail:  francesc.serratosa@urv.cat

Author identifier:  0000000161125913

Journal publication year:  2020

Publication Type:  Journal Publications

ISSN:  13704621

APA:  Santacruz, Pep; Serratosa, Francesc; (2020). Learning the Graph Edit Costs Based on a Learning Model Applied to Suboptimal Graph Matching. Neural Processing Letters, 51(1), 881904. DOI: 10.1007/s1106301910121w

Papper original source:  Neural Processing Letters. 51 (1): 881904

Abstract:  Attributed graphs are used to represent patterns composed of several parts in pattern recognition. The nature of these patterns can be diverse, from images, to handwritten characters, maps or fingerprints. Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. It is based on transforming one graph into another through some edit operations such as substitution, deletion and insertion of nodes and edges. It has two main constraints: it requires an adequate definition of the costs of these operations and its computation cost is exponential with regard to the number of nodes. In this paper, we first present a general framework to automatically learn these edit costs considering graph edit distance is computed in a suboptima way. Then, we specify this framework in two different models based on neural networks and probability density functions. An exhaustive practical validation on 14 public databases, which have different features such as the size of the graphs, the number of attributes or the number of graphs per class have been performed. This validation shows that with the learned edit costs, the accuracy is higher than with some manually imposed costs or other costs automatically learned by previous methods.

Article's DOI:  10.1007/s1106301910121w

Link to the original source:  https://link.springer.com/article/10.1007%2Fs1106301910121w

Papper version:  info:eurepo/semantics/acceptedVersion

licence for use:  https://creativecommons.org/licenses/by/3.0/es/

Department:  Enginyeria Informàtica i Matemàtiques

Licence document URL:  https://repositori.urv.cat/ca/protecciodedades/

Thematic Areas:  Software Neurosciences Neuroscience (miscellaneous) Neuroscience (all) Interdisciplinar General neuroscience Engenharias iv Computer science, artificial intelligence Computer networks and communications Ciência da computação Artificial intelligence

Keywords:  Probability density function Neural network Learning the graph edit costs Graph embedding into vector Graph edit distance

Entity:  Universitat Rovira i Virgili

Record's date:  20230219
