Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Levenshtein Distance for Graph Spectral Features
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Binary Linear Programming Formulation of the Graph Edit Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Assignment Problems
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
The graph neural network model
IEEE Transactions on Neural Networks
A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
Graph edit distance with node splitting and merging, and its application to diatom identification
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Graph Classification and Clustering Based on Vector Space Embedding
Graph Classification and Clustering Based on Vector Space Embedding
Reactive tabu search for measuring graph similarity
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
From maximum common submaps to edit distances of generalized maps
Pattern Recognition Letters
Two new graphs kernels in chemoinformatics
Pattern Recognition Letters
Graph kernels: crossing information from different patterns using graph edit distance
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Efficient processing of graph similarity queries with edit distance constraints
The VLDB Journal — The International Journal on Very Large Data Bases
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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In the field of structural pattern recognition graphs constitute a very common and powerful way of representing objects. The main drawback of graph representations is that the computation of various graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for rather small graphs only. One of the most flexible graph similarity measures is graph edit distance. In this paper we propose a novel approach for the efficient computation of graph edit distance based on bipartite graph matching by means of the Volgenant-Jonker assignment algorithm. Our proposed algorithm provides only suboptimal edit distances, but runs in polynomial time. The reason for its sub-optimality is that edge information is taken into account only in a limited fashion during the process of finding the optimal node assignment between two graphs. In experiments on diverse graph representations we demonstrate a high speed up of our proposed method over a traditional algorithm for graph edit distance computation and over two other sub-optimal approaches that use the Hungarian and Munkres algorithm. Also, we show that classification accuracy remains nearly unaffected by the suboptimal nature of the algorithm.