Improving Hausdorff edit distance using structural node context

Andreas Fischer; Seiichi Uchida; Volkmar Frinken; Kaspar Riesen; Horst Bunke

doi:10.1007/978-3-319-18224-7_15

Improving Hausdorff edit distance using structural node context

Andreas Fischer, Seiichi Uchida, Volkmar Frinken, Kaspar Riesen, Horst Bunke

Real world robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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	https://doi.org/10.1007/978-3-319-18224-7_15
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

Fischer, A., Uchida, S., Frinken, V., Riesen, K., & Bunke, H. (2015). Improving Hausdorff edit distance using structural node context. In B. Luo, W. G. Kropatsch, C-L. Liu, & J. Cheng (Eds.), Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings (pp. 148-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9069). Springer Verlag. https://doi.org/10.1007/978-3-319-18224-7_15

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

Fischer, A, Uchida, S, Frinken, V, Riesen, K & Bunke, H 2015, Improving Hausdorff edit distance using structural node context. in B Luo, WG Kropatsch, C-L Liu & J Cheng (eds), Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9069, Springer Verlag, pp. 148-157, 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015, Beijing, China, 5/13/15. https://doi.org/10.1007/978-3-319-18224-7_15

Fischer A, Uchida S, Frinken V, Riesen K, Bunke H. Improving Hausdorff edit distance using structural node context. In Luo B, Kropatsch WG, Liu C-L, Cheng J, editors, Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings. Springer Verlag. 2015. p. 148-157. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-18224-7_15

Fischer, Andreas ; Uchida, Seiichi ; Frinken, Volkmar ; Riesen, Kaspar ; Bunke, Horst. / Improving Hausdorff edit distance using structural node context. Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings. editor / Bin Luo ; Walter G. Kropatsch ; Cheng-Lin Liu ; Jian Cheng. Springer Verlag, 2015. pp. 148-157 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a9214e9988814285aba870db89107619,

title = "Improving Hausdorff edit distance using structural node context",

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.",

author = "Andreas Fischer and Seiichi Uchida and Volkmar Frinken and Kaspar Riesen and Horst Bunke",

year = "2015",

month = "1",

day = "1",

doi = "10.1007/978-3-319-18224-7_15",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "148--157",

editor = "Bin Luo and Kropatsch, {Walter G.} and Cheng-Lin Liu and Jian Cheng",

booktitle = "Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings",

address = "Germany",

}

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

AN - SCOPUS:84937413776

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 -

Access to Document

10.1007/978-3-319-18224-7_15