  We propose NetGAN - the first implicit generative model for graphs able to
mimic real-world networks. We pose the problem of graph generation as learning
the distribution of biased random walks over the input graph. The proposed
model is based on a stochastic neural network that generates discrete output
samples and is trained using the Wasserstein GAN objective. NetGAN is able to
produce graphs that exhibit well-known network patterns without explicitly
specifying them in the model definition. At the same time, our model exhibits
strong generalization properties, as highlighted by its competitive link
prediction performance, despite not being trained specifically for this task.
Being the first approach to combine both of these desirable properties, NetGAN
opens exciting avenues for further research.
