Distance Patterns in Structural Similarity
The Journal of Machine Learning Research
Exploiting temporal statistics for events analysis and understanding
Image and Vision Computing
ACM Transactions on Computational Logic (TOCL)
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Change detection in ontologies using DAG comparison
CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
Address extraction: extraction of location-based information from the web
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
A DAG comparison algorithm and its application to temporal data warehousing
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
| Hi-index | 0.00 |
The graph is an essential data structure for representing relational information. When graphs are used to represent objects, comparing objects amounts to graph matching. Inexact graph matching is the process of finding the best possible matching between two graphs when exact matching is impossible. In this paper, we propose a new algorithm for the inexact matchingproblem. The new algorithm decomposes the matching process into K phases, where the value of K ranges from 1 to the minimum of the numbers of nodes in the two graphs to be matched. The efficiency of the new algorithm results from the use of small values of K,significantly reducing the search space while still producing very good matchings (most of them optimal) between graphs. The algorithm is compared with the error-correcting subgraph isomorphism algorithm based on A*.