In this paper we propose an inexact graph matching algorithm which computes the dissimilarity of a time-varying labeled graph with respect to a static one. This approach is specifically designed for processing very large labeled graphs, which are subject to frequent edit operations that modify the topology and the labeling of restricted zones of the graph. In this scenario, repeating each time an extensive computation of the whole dissimilarity value would require too much time; moreover, since only a specific part of the graph changes, it would result also in a waste of computations. We propose a fast approach for computing the graph dissimilarity which exploits the dissimilarity value estimated in the previous time interval and the nature of the observed edit operations. We evaluate the properties of the proposed approach with respect to well-known graph matching algorithms, by simulating the dynamics of the graph. Overall, the experiments confirm the effectiveness of the approach.
Matching of time-varying labeled graphs / Bianchi, FILIPPO MARIA; Livi, Lorenzo; Rizzi, Antonello. - (2013), pp. 1-8. (Intervento presentato al convegno 2013 International Joint Conference on Neural Networks, IJCNN 2013 tenutosi a Dallas; United States nel 4 August 2013 through 9 August 2013) [10.1109/ijcnn.2013.6706939].
Matching of time-varying labeled graphs
BIANCHI, FILIPPO MARIA;LIVI, LORENZO;RIZZI, Antonello
2013
Abstract
In this paper we propose an inexact graph matching algorithm which computes the dissimilarity of a time-varying labeled graph with respect to a static one. This approach is specifically designed for processing very large labeled graphs, which are subject to frequent edit operations that modify the topology and the labeling of restricted zones of the graph. In this scenario, repeating each time an extensive computation of the whole dissimilarity value would require too much time; moreover, since only a specific part of the graph changes, it would result also in a waste of computations. We propose a fast approach for computing the graph dissimilarity which exploits the dissimilarity value estimated in the previous time interval and the nature of the observed edit operations. We evaluate the properties of the proposed approach with respect to well-known graph matching algorithms, by simulating the dynamics of the graph. Overall, the experiments confirm the effectiveness of the approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.