Efficient approximate approach for graph edit distance problem
Graph Edit Distance (GED) problem is a well-known tool used to measure the similarity/dissimilarity between two graphs. It searches for the best set of edit operations (in terms of cost) that transforms one graph into another. Due to the NP-hardness nature of the problem, the search space increases exponentially making exact approaches impossible to use for large graphs. In this context, there is a huge need for approaches that give near-optimal results in reasonable time. In this paper, we propose a tree-based approximate approach for dealing with GED problem. It operates on a search tree that models all possible solutions of the problem. Since exploring the whole tree is impractical; this approach keeps only the best nodes at each level of the tree for further exploration. This reduces enormously the execution time without scarifying the solution quality. Experiments using small and medium size data-sets show the low deviation of our results as compared to the optimal results of a Depth First Search algorithm. Moreover, our approach show a strong scalability potential by dealing with large data-sets in low execution time.
Graph matching, Graph edit distance, Approximate approach, Pattern recognition