AlignGraph: A Group of Generative Models for Graphs
Kimia Shayestehfard, Dana Brooks, Stratis Ioannidis

TL;DR
AlignGraph introduces a novel framework combining efficient graph alignment with permutation-invariant generative models, significantly improving the ability to learn graph distributions compared to existing methods.
Contribution
It presents a new group of generative models that effectively address permutation invariance in graph generation, outperforming previous approaches.
Findings
Successfully learns graph distributions with high accuracy.
Outperforms competitors by 25%-560% in relevant scores.
Demonstrates efficiency and effectiveness of the proposed alignment-based approach.
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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Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling
