Handbook of graph grammars and computing by graph transformation: volume I. foundations
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Term Graph Rewriting and Mobile Expressions in Functional Languages
AGTIVE '99 Proceedings of the International Workshop on Applications of Graph Transformations with Industrial Relevance
Grammatical Inference Based on Hyperedge Replacement
Proceedings of the 4th International Workshop on Graph-Grammars and Their Application to Computer Science
Specification of Graph Translators with Triple Graph Grammars
WG '94 Proceedings of the 20th International Workshop on Graph-Theoretic Concepts in Computer Science
Mining Graph Data
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Spatial graph grammars for graphical user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Graph Grammar Induction on Structural Data for Visual Programming
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
MARS: A metamodel recovery system using grammar inference
Information and Software Technology
Transforming Scene Graphs Using Triple Graph Grammars --- A Practice Report
Applications of Graph Transformations with Industrial Relevance
Model-Driven Software Development with Graph Transformations: A Comparative Case Study
Applications of Graph Transformations with Industrial Relevance
Inferring Graph Grammars by Detecting Overlap in Frequent Subgraphs
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
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A graph grammar is a generative description of a graph language (a possibly infinite set of graphs). In this paper, we present a novel algorithm for inducing a graph grammar from a given set of 'positive' and 'negative' graphs. The algorithm is guaranteed to produce a grammar that can generate all of the positive and none of the negative input graphs. Driven by a heuristic specific-to-general search process, the algorithm tries to find a small grammar that generalizes beyond the positive input set. During the search, the algorithm employs a graph grammar parser to eliminate the candidate grammars that can generate at least one negative input graph. We validate our method by inducing grammars for chemical structural formulas and flowcharts and thereby show its potential applicability to chemical engineering and visual programming.