Inferring Probability Distributions of Graph Size and Node Degree from Stochastic Graph Grammars
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Summary
It is shown that a stochastic graph grammar can be used to efficiently compute the probability mass functions of the number of nodes, thenumber of edges, and the degree of a node selected uniformly at random from a graph sampled from the distribution defined by the graph grammar.
- Type
- article
- Published
- 2010-04-29
- Cited by
- 1
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W33544593
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7141468
Keywords
Computer science, Rule-based machine translation, Graph, Graph property, Random graph
References
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- Grammatical inference by hill climbing
- Inference of Edge Replacement Graph Grammars
- Precise N-Gram Probabilities From Stochastic Context-Free Grammars
- New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron
- gSpan: graph-based substructure pattern mining
- The estimation of stochastic context-free grammars using the Inside-Outside algorithm
- On Graph Kernels: Hardness Results and Efficient Alternatives
- Handbook of Graph Grammars and Computing by Graph Transformation
- Inducing Probabilistic Grammars by Bayesian Model Merging
- Grammatical Inference Based on Hyperedge Replacement
- Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars
- The Power of Amnesia
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