@InProceedings{CI-Blumenthal-2019,


author = {David Blumenthal and Sébastien Bougleux and Johann Gamper and Luc Brun},
title = {GEDLIB: A C++ Library for Graph Edit Distance Computation},
booktitle = {12th IAPR TC15 Workshop on Graph-Based Representation in Pattern Recognition (GbR)},
year = 2019,
editor = {Donatello Conte and Jean-Yves Ramel and Pasquale Foggia},
volume = 11510,
series = {LNCS},
pages = {14-24},
month = {June},
address = {Tours},
organization = {IAPR TC15},
publisher = {Springer},
url={HAL:=https://hal-normandie-univ.archives-ouvertes.fr/hal-02162839, Python Binding(GIT):=https://forge.greyc.fr/projects/gedlibpy/repository},
theme={pattern,ged},
abstract={The graph edit distance (GED) is a flexible graph dissimilarity measure widely used within the structural pattern recognition field. In this paper, we present GEDLIB, a C++ library for exactly or approximately computing GED. Many existing algorithms for GED are already implemented in GEDLIB. Moreover, GEDLIB is designed to be easily extensible: for implementing new edit cost functions and GED algorithms, it suffices to implement abstract classes contained in the library. For implementing these extensions, the user has access to a wide range of utilities, such as deep neural networks, support vector machines, mixed integer linear programming solvers, a blackbox optimizer, and solvers for the linear sum assignment problem with and without error-correction}

 }