Structure-based graph distance measures of high degree of precision
Pattern Recognition
Efficient Clustering of Structured Documents Using Graph Self-Organizing Maps
Focused Access to XML Documents
Graph self-organizing maps for cyclic and unbounded graphs
Neurocomputing
A reinforced iterative formalism to learn from human errors and uncertainty
Engineering Applications of Artificial Intelligence
Fast generation of local Hasse graphs for learning from structurally connected instances
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Indexing tree and subtree by using a structure network
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Improving vector space embedding of graphs through feature selection algorithms
Pattern Recognition
Automatic learning of edit costs based on interactive and adaptive graph recognit
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Graph matching – challenges and potential solutions
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
A relational-based approach for aggregated search in graph databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Discriminative prototype selection methods for graph embedding
Pattern Recognition
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Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.