The Normalized String Editing Problem Revisited
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
An Algorithm for Finding the Largest Approximately Common Substructures of Two Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spectral Algorithm for Seriation and the Consecutive Ones Problem
SIAM Journal on Computing
The String-to-String Correction Problem
Journal of the ACM (JACM)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Spectral Feature Vectors for Graph Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Maximum Likelihood Motion Segmentation Using Eigendecomposition
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Comparing string representations and distances in a natural images classification task
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
| Hi-index | 0.01 |
This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that it lacks the formality and rigour of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this we use graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as maximum a posteriori probability alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which for edit cost is the negative logarithm of the a posteriori sequence alignment probability. To compute the string alignment probability we provide models of the edge compatibility error and the probability of individual node correspondences.