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A Parallel Graph Edit Distance Algorithm


Graph edit distance (GED) has emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning, and data mining. GED is an error-tolerant graph matching technique that represents the minimum-cost sequence of basic editing operations to transform a graph into another graph by means of insertion, deletion and substitution of nodes or edges. Unfortunately, GED is a NP-hard combinatorial optimization problem. The question of elaborating fast and precise algorithms is of first interest. In this paper, a parallel algorithm for exact GED computation is proposed. Our proposal is based on a branch-and-bound algorithm coupled with a load balancing strategy. Parallel threads run a branch-and-bound algorithm to explore the solution space and to discard misleading partial solutions. In the mean time, the load balancing scheme ensures that no thread remains idle. Experiments on 4 publicly available datasets empirically demonstrated that under time constraints our proposal can drastically improve a sequential approach and a naive parallel approach. Our proposal was compared to 6 other methods and provided more precise solutions while requiring a low memory usage. Experiments also showed that having more precise solutions does not necessarily lead to higher classification rates. Such a result raises a question of the benefit of having more precise solutions in a classification context.
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hal-01476393 , version 1 (24-02-2017)


  • HAL Id : hal-01476393 , version 1


Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick Martineau. A Parallel Graph Edit Distance Algorithm. 2017. ⟨hal-01476393⟩
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