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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance
verfasst von : Xavier Cortés, Donatello Conte, Hubert Cardot, Francesc Serratosa
Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition
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Abstract
The problem of finding a distance and a correspondence between a pair of graphs is commonly referred to as the Error-tolerant Graph matching problem. The Graph Edit Distance is one of the most popular approaches to solve this problem. This method needs to define a set of parameters and the cost functions aprioristically. On the other hand, in recent years, Deep Neural Networks have shown very good performance in a wide variety of domains due to their robustness and ability to solve non-linear problems. The aim of this paper is to present a model to compute the assignments costs for the Graph Edit Distance by means of a Deep Neural Network previously trained with a set of pairs of graphs properly matched. We empirically show a major improvement using our method with respect to the state-of-the-art results.