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arXiv · 2301.11273

AlignGraph: A Group of Generative Models for Graphs

Kimia Shayestehfard·Dana Brooks·Stratis Ioannidis
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Abstract

It is challenging for generative models to learn a distribution over graphs because of the lack of permutation invariance: nodes may be ordered arbitrarily across graphs, and standard graph alignment is combinatorial and notoriously expensive. We propose AlignGraph, a group of generative models that combine fast and efficient graph alignment methods with a family of deep generative models that are invariant to node permutations. Our experiments demonstrate that our framework successfully learns graph distributions, outperforming competitors by 25% -560% in relevant performance scores.

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Kimia Shayestehfard, Dana Brooks, Stratis Ioannidis. 2023-01-26. AlignGraph: A Group of Generative Models for Graphs. https://arxiv.org/abs/2301.11273

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