In this paper we face the inexact graph matching problem from the parallel algorithms viewpoint. After a brief introduction of both graph matching and parallel computing contexts, we discuss a specific method of performing inexact graph matching based on the well known tensor product operator. We analyze the problem using two parallel computing models, following different algorithmic strategies, and performing also an experimental evaluation. The aim of this paper is to provide modeling and algorithmic strategies to extend inexact graph matching methods to graphs of high order and size, conceiving the computational problem in the more wider context of graph-based Pattern Recognition and Soft Computing systems. As a whole, the obtained results encourage more effort on this direction. © 2012 IEEE.
Parallel Algorithms for Tensor Product-based Inexact Graph Matching / Livi, Lorenzo; Rizzi, Antonello. - (2012), pp. 1-8. (Intervento presentato al convegno IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI) tenutosi a Brisbane; Australia nel JUN 10-15, 2012) [10.1109/ijcnn.2012.6252681].
Parallel Algorithms for Tensor Product-based Inexact Graph Matching
LIVI, LORENZO;RIZZI, Antonello
2012
Abstract
In this paper we face the inexact graph matching problem from the parallel algorithms viewpoint. After a brief introduction of both graph matching and parallel computing contexts, we discuss a specific method of performing inexact graph matching based on the well known tensor product operator. We analyze the problem using two parallel computing models, following different algorithmic strategies, and performing also an experimental evaluation. The aim of this paper is to provide modeling and algorithmic strategies to extend inexact graph matching methods to graphs of high order and size, conceiving the computational problem in the more wider context of graph-based Pattern Recognition and Soft Computing systems. As a whole, the obtained results encourage more effort on this direction. © 2012 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
