Abstract
Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. One of its main constraints is that it requires an adequate definition of edit costs, which are application dependent. These costs eventually determine which graphs are considered similar or not in a concrete application. Several methods have been presented to learn these costs to avoid manually setting them. They are based on different techniques ranging from probabilistic methods to neural networks or known optimisation algorithms. The aim of this paper is twofold. On the one hand, we list them and summarize their features. On the other hand, we empirically analyse the behaviour of the proposed optimisation functions. We conclude that these functions return different edit costs and therefore, they have to be considered application dependent and not only a technicality of the method, as it has been considered so far.
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Acknowledgments
This research is supported by projects TIN2016-77836-C2-1-R, DPI2016-78957-R AEI/FEDER EU; and the European projects AEROARMS, H2020-ICT-2014-1-644271 and NanoInformaTIX, H2020-NMBP-TO-IND-2018-2020-814426.
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Rica, E., Álvarez, S., Serratosa, F. (2019). Learning the Graph Edit Costs: What Do We Want to Optimise?. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_3
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