A Generative Model of Complex Networks Using Graphons and Neural Inverse Operators
Generative graph models are central to understanding and simulating complex networks. However, existing approaches have complementary strengths and limitations. Mechanistic models offer interpretability but rely on instance-specific estimation methods. Deep generative models, on the other hand, offer amortized inferenc...
W. Choi, I. Pinto, Chen Sun et al.
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