Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity learning for graph-based image representations
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph embedding using tree edit-union
Pattern Recognition
Graph Characteristics from the Ihara Zeta Function
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
Graph kernels based on tree patterns for molecules
Machine Learning
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Graph Matching Based on Node Signatures
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
The Journal of Machine Learning Research
Graph embedding using constant shift embedding
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Two new graphs kernels in chemoinformatics
Pattern Recognition Letters
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Defining similarities or distances between graphs is one of the bases of the structural pattern recognition field. An important trend within this field consists in going beyond the simple formulation of similarity measures by studying properties of graph's spaces induced by such distance or similarity measures . Such a problematic is closely related to the graph embedding problem. In this article, we investigate two types of similarity measures. The first one is based on the notion of graph edit distance which aims to catch a global dissimilarity between graphs. The second family is based on comparisons of bags of patterns extracted from graphs to be compared. Both approaches are detailed and their performances are evaluated on different chemoinformatics problems.