graphons

This USC paper proposes a generative model for complex networks that combines mechanistic interpretability with amortized inference: a multifractal step graphon parameterizes networks, while a neural inverse operator recovers parameters on unseen graph sizes. The method outperforms a graph foundation model on zero-shot generation benchmarks and detects brain-state changes in multi-subject EEG more sensitively than traditional network statistics.