Non-Confluent NLC Graph Grammar Inference by Compressing Disjoint Subgraphs

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Grammar inference deals with determining (preferable simple) models/grammars consistent with a set of observations. There is a large body of research on grammar inference within the theory of formal languages. However, there is surprisingly little known on grammar inference for graph grammars. In this paper we take a further step in this direction and work within the framework of node label controlled (NLC) graph grammars. Specifically, we characterize, given a set of disjoint and isomorphic subgraphs of a graph $G$, whether or not there is a NLC graph grammar rule which can generate these subgraphs to obtain $G$. This generalizes previous results by assuming that the set of isomorphic subgraphs is disjoint instead of non-touching. This leads naturally to consider the more involved ``non-confluent'' graph grammar rules.
12 pages, 1 figure

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Machine Learning, Discrete Mathematics

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http://salesiana.dossiersoluciones.com/handle/123456789/218113

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0901.4876
 http://arxiv.org/abs/0901.4876

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arXiv