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1 LI - Laboratoire d-Informatique de l-Université de Tours

Abstract : 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.

Keywords : Graph Matching Parallel Computing Graph Edit Distance Pattern Recognition Load Balancing

Author: Zeina Abu-Aisheh - Romain Raveaux - Jean-Yves Ramel - Patrick Martineau -

Source: https://hal.archives-ouvertes.fr/


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