# Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

@article{Stoehr2019DisentanglingIG, title={Disentangling Interpretable Generative Parameters of Random and Real-World Graphs}, author={Niklas Stoehr and Marc Brockschmidt and Jan Stuehmer and Emine Yilmaz}, journal={ArXiv}, year={2019}, volume={abs/1910.05639} }

While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem. Devising generative models that closely reproduce real-world graphs requires domain knowledge and time-consuming simulation. While existing deep learning approaches rely on less manual modelling, they offer little interpretability. This work approaches graph generation (decoding) as the inverse of graph…

## 11 Citations

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