AlignGraph: A Group of Generative Models for Graphs

• URL: http://arxiv.org/abs/2301.11273v1
• Date: Thu, 26 Jan 2023 18:28:40 GMT
• Title: AlignGraph: A Group of Generative Models for Graphs
• Authors: Kimia Shayestehfard, Dana Brooks, Stratis Ioannnidis
• Abstract summary: 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 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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