A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Median Graphs: Properties, Algorithms, and Applications
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Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Median graph computation for graph clustering
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Closure-Tree: An Index Structure for Graph Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Evaluation of Spectral-Based Methods for Median Graph Computation
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
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
Median graphs: A genetic approach based on new theoretical properties
Pattern Recognition
Graduated assignment algorithm for finding the common labelling of a set of graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthesis of median spectral graph
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Graph embedding in vector spaces by node attribute statistics
Pattern Recognition
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
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Structural pattern recognition is a well-know research field that has its birth in the early 80s. Throughout 30 years, structures such as graphs have been compared through optimization of functions that directly use attribute values on nodes and arcs. Nevertheless, in the last decade, kernel and embedding methods appeared. These new methods deduct a similarity value and a final labelling between nodes through representing graphs into a multi-dimensional space. It seems that lately kernel and embedding methods are preferred with respect to classical structural methods. However, both approaches have advantages and drawbacks. In this work, we compare structural methods to embedding and kernel methods. Results show that, with the evaluated datasets, some structural methods give slightly better performance and therefore, it is still early to discard classical structural methods for graph pattern recognition.