Graph Edit Distance with Node Splitting and Merging, and Its Application to Diatom Identification

Ambauen, R.; Fischer, Stefan; Bunke, Horst

doi:10.1007/3-540-45028-9_9

Cited by 76 publications

(60 citation statements)

References 13 publications

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“…algorithms that would consider the possibility of a node being split into multiple ones or multiple nodes being merged into one. Such graph matching algorithms have been considered in other application domains [2]. We plan to investigate such variants in future work.…”

Section: Resultsmentioning

confidence: 99%

Graph Matching Algorithms for Business Process Model Similarity Search

Dijkman

Dumas

García‐Bañuelos

2009

Lecture Notes in Computer Science

295

243

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Abstract. We investigate the problem of ranking all process models in a repository according to their similarity with respect to a given process model. We focus specifically on the application of graph matching algorithms to this similarity search problem. Since the corresponding graph matching problem is NP-complete, we seek to find a compromise between computational complexity and quality of the computed ranking. Using a repository of 100 process models, we evaluate four graph matching algorithms, ranging from a greedy one to a relatively exhaustive one. The results show that the mean average precision obtained by a fast greedy algorithm is close to that obtained with the most exhaustive algorithm.

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Section: Resultsmentioning

confidence: 99%

Graph Matching Algorithms for Business Process Model Similarity Search

Dijkman

Dumas

García‐Bañuelos

2009

Lecture Notes in Computer Science

295

243

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“…While some potential solutions have been proposed for the case of string edit distance [12], the edit costs in graph matching are still manually set in a heuristic trial and error procedure, exploiting problem specific knowledge. For examples see [13,14]. In this section we briefly outline a novel procedure for the automatic learning of the costs of graph edit operations from a set of sample graphs [15].…”

Section: Automatic Learning Of Edit Cost Functionsmentioning

confidence: 99%

Graph Matching – Challenges and Potential Solutions

Bunke

Irniger

Neuhaus

2005

Image Analysis and Processing – ICIAP 2005

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Abstract. Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.

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“…Hence, it is easy to observe that the computational complexity of the graph edit distance algorithm is exponential in the number of nodes involved. Nonetheless, for small graphs it has proven a powerful graph similarity measure [9,10]. But for large graphs it becomes computationally infeasible due to its high running time and memory complexity.…”

Section: Graph Edit Distancementioning

confidence: 99%

An Error-Tolerant Approximate Matching Algorithm for Attributed Planar Graphs and Its Application to Fingerprint Classification

Neuhaus

Bunke

2004

Lecture Notes in Computer Science

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Abstract. Graph edit distance is a powerful error-tolerant similarity measure for graphs. For pattern recognition problems involving large graphs, however, the high computational complexity makes it sometimes impossible to apply edit distance algorithms. In the present paper we propose an efficient algorithm for edit distance computation of planar graphs. Given graphs embedded in the plane, we iteratively match small subgraphs by locally optimizing structural correspondences. Eventually we obtain a valid edit path and hence an upper bound of the edit distance. To demonstrate the efficiency of our approach, we apply the proposed algorithm to the problem of fingerprint classification.

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Graph Edit Distance with Node Splitting and Merging, and Its Application to Diatom Identification

Cited by 76 publications

References 13 publications

Graph Matching Algorithms for Business Process Model Similarity Search

Graph Matching Algorithms for Business Process Model Similarity Search

Graph Matching – Challenges and Potential Solutions

An Error-Tolerant Approximate Matching Algorithm for Attributed Planar Graphs and Its Application to Fingerprint Classification

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