A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error-Tolerant Graph Matching: A Formal Framework and Algorithms
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Deterministic Search Stragtegies for Relational Graph Matching
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Approximation on the Web: A Compendium of NP Optimization Problems
RANDOM '97 Proceedings of the International Workshop on Randomization and Approximation Techniques in Computer Science
Differencing and merging of architectural views
Automated Software Engineering
Recovering the Evolution Stable Part Using an ECGM Algorithm: Is There a Tunnel in Mozilla?
CSMR '09 Proceedings of the 2009 European Conference on Software Maintenance and Reengineering
Using local similarity measures to efficiently address approximate graph matching
Discrete Applied Mathematics
| Hi-index | 0.00 |
In this paper, we investigate heuristics in order to solve the Approximated Matching Problem (AGM). We propose a tabu search algorithm which exploits a simple neighborhood but is initialized by a greedy procedure which uses a measure of similarity between the vertices of the two graphs. The algorithm is tested on a large collection of graphs of various sizes (from 300 vertices and up to 3000 vertices) and densities. Computing times range from less than 1 second up to a few minutes. The algorithm obtains consistently very good results, especially on labeled graphs. The results obtained by the tabu algorithm alone (without the greedy procedure) were very poor, illustrating the importance of using vertex similarity during the early steps of the search process.