Paper ID: 2301.11273

AlignGraph: A Group of Generative Models for Graphs

Kimia Shayestehfard, Dana Brooks, Stratis Ioannidis

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.

Submitted: Jan 26, 2023