GraphGAN: Generating Graphs via Random Walks

ICLR 2018 Aleksandar BojchevskiOleksandr ShchurDaniel ZügnerStephan Günnemann

We propose GraphGAN - the first implicit generative model for graphs that enables to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over a single input graph... (read more)

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