2023/01/26 by Kimia Shayestehfard, Shayestehfard, Kimia, Dana H. Brooks +3 · 1 citation
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Combinatorics #Computer science #FOS: Computer and information sciences #Generative grammar #Generative model #Graph #Invariant (physics) #Machine Learning (cs.LG) #Mathematics #Permutation (music) #Social and Information Networks (cs.SI) #Theoretical computer science #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2301.11273
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.