Abstract
The present paper is concerned with graph edit distance, which is widely accepted as one of the most flexible graph dissimilarity measures available. A recent algorithmic framework for approximating the graph edit distance overcomes the major drawback of this distance model, viz. its exponential time complexity. Yet, this particular approximation suffers from an overestimation of the true edit distance in general. Overall aim of the present paper is to improve the distance quality of this approximation by means of a post-processing search procedure. The employed search procedure is based on the idea of simulated annealing, which turns out to be particularly suitable for complex optimization problems. In an experimental evaluation on several graph data sets the benefit of this extension is empirically confirmed.
Notes
- 1.
A similar notation is used for edges.
- 2.
Edit operations of the form \((\varepsilon \rightarrow \varepsilon )\) can be dismissed, of course.
- 3.
- 4.
On the other data sets very similar plots can be observed.
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This work has been supported by the Hasler Foundation Switzerland.
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Riesen, K., Fischer, A., Bunke, H. (2017). Improved Graph Edit Distance Approximation with Simulated Annealing. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_20
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