Fast Suboptimal Algorithms for the Computation of Graph Edit Distance (original) (raw)
Abstract
Graph edit distance is one of the most flexible mechanisms for error-tolerant graph matching. Its key advantage is that edit distance is applicable to unconstrained attributed graphs and can be tailored to a wide variety of applications by means of specific edit cost functions. Its computational complexity, however, is exponential in the number of vertices, which means that edit distance is feasible for small graphs only. In this paper, we propose two simple, but effective modifications of a standard edit distance algorithm that allow us to suboptimally compute edit distance in a faster way. In experiments on real data, we demonstrate the resulting speedup and show that classification accuracy is mostly not affected. The suboptimality of our methods mainly results in larger inter-class distances, while intra-class distances remain low, which makes the proposed methods very well applicable to distance-based graph classification.
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Authors and Affiliations
- Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012, Bern, Switzerland
Michel Neuhaus, Kaspar Riesen & Horst Bunke
Authors
- Michel Neuhaus
- Kaspar Riesen
- Horst Bunke
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Editors and Affiliations
- Hong Kong University of Science and Technology,
Dit-Yan Yeung - Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
James T. Kwok - Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
Ana Fred - Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
Fabio Roli - Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
Dick de Ridder
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Neuhaus, M., Riesen, K., Bunke, H. (2006). Fast Suboptimal Algorithms for the Computation of Graph Edit Distance. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_17
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