On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
The Amsterdam Library of Object Images
International Journal of Computer Vision
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Graph Classification and Clustering Based on Vector Space Embedding
Graph Classification and Clustering Based on Vector Space Embedding
Graph embedding in vector spaces by node attribute statistics
Pattern Recognition
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
Graph Characterization via Ihara Coefficients
IEEE Transactions on Neural Networks
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Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.