While the performance of DDGMs is impressive, not all of their aspects are fully understood.

Why do many modern neural-network-based graph generative models fail to reproduce typical real-world network characteristics, such as high triangle density?

Weshowthat thiscansuccessfully yield "one-shot" knowledge transfer (i.e, without requiring any retraining) between neural networks trained on heterogeneous non-i.i.d.

In accelerate restricted 51, 50].

In this paper, we will give the non-asymptotic convergence rate of the prediction error in high-dimensional adversarial training.