Abstract: Recent work in graph
models has found that probabilistic hyperedge replacement grammars (HRGs) can be extracted from
graphs and used to generate new random
graphs with graph properties and substructures close to the original. In this paper, we show how to add latent variables to the model, trained using Expectation-Maximization, to generate still better
graphs, that is, ones that ...
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