Abstract
Graph edit distance corresponds to a flexible graph dissimilarity measure. Unfortunately, its computation requires an exponential complexity according to the number of nodes of both graphs being compared. Some heuristics based on bipartite assignment algorithms have been proposed in order to approximate the graph edit distance. However, these heuristics lack of accuracy since they are based either on small patterns providing a too local information or walks whose tottering induce some bias in the edit distance calculus. In this work, we propose to extend previous heuristics by considering both less local and more accurate patterns using subgraphs defined around each node.
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Carletti, V., Gaüzère, B., Brun, L., Vento, M. (2015). Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_19
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DOI: https://doi.org/10.1007/978-3-319-18224-7_19
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