On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Median graph computation for graph clustering
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Mixing spectral representations of graphs
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Applied Graph Theory in Computer Vision and Pattern Recognition (Studies in Computational Intelligence)
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Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.