Title :
A neural supergranph matching architecture
Author :
Klinger, Stefan ; Austin, Jim
Author_Institution :
Dept. of Comput. Sci., York Univ.
fDate :
July 31 2005-Aug. 4 2005
Abstract :
A neural supergraph matching architecture is introduced based on relaxation labeling and the minimum common supergraph of pairs of graphs. The system is implemented on correlation matrix memories and is efficient in constructing this supergraph. We test the effectiveness of this graphical cluster representation on two different sets of graphs
Keywords :
graph theory; pattern matching; correlation matrix memories; graphical cluster representation; neural supergraph matching architecture; relaxation labeling; Approximation algorithms; Clustering algorithms; Computer architecture; Computer science; Data mining; Electronic mail; Joining processes; Labeling; Size measurement; Testing;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556287
