Improving Hausdorff Edit Distance Using Structural Node Context
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Authors
Author (Corporation)
Publication date
2015
Typ of student thesis
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Collections
Type
04B - Conference paper
Editors
Liu, Cheng-Lin
Luo, Bin
Kropatsch, Walter G.
Cheng, Jian
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Parent work
Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 International Workshop, GbRPR 2015, Beijing, China, May 13-15, 2015.
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Lecture Notes in Computer Science
Series number
9069
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Springer
Place of publication / Event location
Hamburg
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Abstract
In order to cope with the exponential time complexity of graph edit distance, several polynomial-time approximation algorithms have been proposed in recent years. The Hausdorff edit distance is a quadratic-time matching procedure for labeled graphs which reduces the edit distance to a correspondence problem between local substructures. In its original formulation, nodes and their adjacent edges have been considered as local substructures. In this paper, we integrate a more general structural node context into the matching procedure based on hierarchical subgraphs. In an experimental evaluation on diverse graph data sets, we demonstrate that the proposed generalization of Hausdorff edit distance can significantly improve the accuracy of graph classification while maintaining low computational complexity.
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ISBN
978-3-319-18223-0
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Language
German
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Yes
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Published
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Citation
Fischer, A., Uchida, S., Frinken, V., Riesen, K., & Bunke, H. (2015). Improving Hausdorff Edit Distance Using Structural Node Context. In C.-L. Liu, B. Luo, W. G. Kropatsch, & J. Cheng (eds.), Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 International Workshop, GbRPR 2015, Beijing, China, May 13-15, 2015. Springer. http://hdl.handle.net/11654/8238