Self-organizing maps
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph distances using graph union
Pattern Recognition Letters
A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
Introduction to the Special Section on Graph Algorithms in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Graph edit distance with node splitting and merging, and its application to diatom identification
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Graph-based tools for data mining and machine learning
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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This paper addresses 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 representing attribute distance spaces that encode edit operation costs. The self-organizing maps are iteratively adapted to minimize the edit distance of those graphs that are required to be similar. To demonstrate the learning effect, the distance model is applied to graphs representing line drawings and diatoms.