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It is challenging for generative models to learn a distribution over graphsbecause of the lack of permutation invariance: nodes may be ordered arbitrarilyacross graphs, and standard graph alignment is combinatorial and notoriouslyexpensive. We propose AlignGraph, a group of generative models that combinefast and efficient graph alignment methods with a family of deep generativemodels that are invariant to node permutations. Our experiments demonstratethat our framework successfully learns graph distributions, outperformingcompetitors by 25% -560% in relevant performance scores.

AlignGraph: A Group of Generative Models for Graphs