On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
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
Graph distances using graph union
Pattern Recognition Letters
A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Feature-based similarity search in graph structures
ACM Transactions on Database Systems (TODS)
Exploiting structural similarity for effective Web information extraction
Data & Knowledge Engineering
Retrieval of objects in video by similarity based on graph matching
Pattern Recognition Letters
Bipartite graph matching for computing the edit distance of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Self-organizing maps for learning the edit costs in graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In recent years, evaluating graph distance has become more and more important in a variety of real applications and many graph distance measures have been proposed. Among all of those measures, structure-based graph distance measures have become the research focus due to their independence of the definition of cost functions. However, existing structure-based graph distance measures have low degree of precision because only node and edge information of graphs are employed in these measures. To improve the precision of graph distance measures, we define substructure abundance vector (SAV) to capture more substructure information of a graph. Furthermore, based on SAV, we propose unified graph distance measures which are generalization of the existing structure-based graph distance measures. In general, the unified graph distance measures can evaluate graph distance in much finer grain. We also show that unified graph distance measures based on occurrence mapping and some of their variants are metrics. Finally, we apply the unified graph distance metric and its variants to the population evolution analysis and construct distance graphs of marker networks in three populations, which reflect the single nucleotide polymorphism (SNP) linkage disequilibrium (LD) differences among these populations.