@InProceedings{CI-blumenthal18-2,
author = {David Blumenthal and S'ebastien Bougleux and Johann Gamper and Luc Brun}, title = {Quasimetric Graph Edit Distance As a Compact Quadratic Assignment Problem}, booktitle = {Proceedings of ICPR 2018}, year = 2018, pages = {934-939}, month = {August}, address = {Beijing, China}, organization = {IAPR}, publisher = {IEEE}, note={ISBN: ISBN: 978-1-5386-3787-6}, theme={pattern,ged}, url={HAL:=https://hal-normandie-univ.archives-ouvertes.fr/hal-01865214, HAL(PDF):= https://hal-normandie-univ.archives-ouvertes.fr/hal-01865214/document}, abstract={The graph edit distance (GED) is a widely used distance measure for attributed graphs. It has recently been shown that the problem of computing GED, which is a NP-hard optimization problem, can be formulated as a quadratic assignment problem (QAP). This formulation is useful, since it allows to derive well performing approximative heuristics for GED from existing techniques for QAP. In this paper, we focus on the case where the edit costs that underlie GED are quasimetric. This is the case in many applications of GED. We show that, for quasimetric edit costs, it is possible to reduce the size of the corresponding QAP formulation. An empirical evaluation shows that this reduction significantly speeds up the QAP-based approximative heuristics for GED. }
}