Abstract
This paper addresses the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps representing attribute distance spaces that encode edit operation costs. The self-organizing maps are iteratively adapted to minimize the edit distance of those graphs that are required to be similar. To demonstrate the learning effect, the distance model is applied to graphs representing line drawings and diatoms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
IEEE Transactions on Pattern Analysis and Machine Intelligence: Special section on graph algorithms and computer vision. 23 (2001) 1040–1151
Pattern Recognition Letters: Special issue on graph based representations. 24 (2003) 1033–1122
Int. Journal of Pattern Recognition and Art. Intelligence: Special issue on graph matching in pattern recognition and computer vision. (In preparation)
Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters 19 (1998) 255–259
Wallis, W., Shoubridge, P., Kraetzl, M., Ray, D.: Graph distances using graph union. Pattern Recognition Letters 22 (2001) 701–704
Fernandez, M.L., Valiente, G.: A graph distance metric combining maximum common subgraph and minimum common supergraph. Pattern Recognition Letters 22 (2001) 753–758
Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics 13 (1983) 353–363
Myers, R., Wilson, R., Hancock, E.: Bayesian graph edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 628–635
Messmer, B., Bunke, H.: A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 493–504
Kohonen, T.: Self-Organizing Maps. Springer (1995)
Ambauen, R., Fischer, S., Bunke, H.: Graph edit distance with node splitting and merging and its application to diatom identification (2003) Submitted to GbR 2003.
Günter, S., Bunke, H.: Self-organizing map for clustering in the graph domain. Pattern Recognition Letters 23 (2002) 405–417
Neuhaus, M.: Learning graph edit distance. Master’s thesis, University of Bern, Switzerland (2002)
Günter, S., Bunke, H.: Validation indices for graph clustering. Pattern Recognition Letters 24 (2003) 1107–1113
du Buf, H., Bayer, M., eds.: Automatic Diatom Identification. World Scientific (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neuhaus, M., Bunke, H. (2003). Self-Organizing Graph Edit Distance. In: Hancock, E., Vento, M. (eds) Graph Based Representations in Pattern Recognition. GbRPR 2003. Lecture Notes in Computer Science, vol 2726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45028-9_8
Download citation
DOI: https://doi.org/10.1007/3-540-45028-9_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40452-1
Online ISBN: 978-3-540-45028-3
eBook Packages: Springer Book Archive