NetGAN: Generating Graphs via Random Walks
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:609618, 2018.
Abstract
We propose NetGAN  the first implicit generative model for graphs able to mimic realworld 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 wellknown 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.
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