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.
| Original language | English |
|---|---|
| Title of host publication | Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings |
| Editors | Bin Luo, Walter G. Kropatsch, Cheng-Lin Liu, Jian Cheng |
| Publisher | Springer Verlag |
| Pages | 148-157 |
| Number of pages | 10 |
| ISBN (Electronic) | 9783319182230 |
| DOIs | |
| Publication status | Published - Jan 1 2015 |
| Event | 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015 - Beijing, China Duration: May 13 2015 → May 15 2015 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9069 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 5/13/15 → 5/15/15 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Improving Hausdorff edit distance using structural node context. / Fischer, Andreas; Uchida, Seiichi; Frinken, Volkmar; Riesen, Kaspar; Bunke, Horst.
Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings. ed. / Bin Luo; Walter G. Kropatsch; Cheng-Lin Liu; Jian Cheng. Springer Verlag, 2015. p. 148-157 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9069).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Improving Hausdorff edit distance using structural node context
AU - Fischer, Andreas
AU - Uchida, Seiichi
AU - Frinken, Volkmar
AU - Riesen, Kaspar
AU - Bunke, Horst
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84937413776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937413776&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18224-7_15
DO - 10.1007/978-3-319-18224-7_15
M3 - Conference contribution
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 148
EP - 157
BT - Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings
A2 - Luo, Bin
A2 - Kropatsch, Walter G.
A2 - Liu, Cheng-Lin
A2 - Cheng, Jian
PB - Springer Verlag
ER -