2019/10/17 by Tony Duan, Duan, Tony, Juho Lee +1
Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1910.08057
Presented at the NeurIPS 2019 Workshop on Graph Representation Learning
arxiv created 2019/10/17 · openalex publication_date 2019/10/17 · arxiv updated 2019/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes the graph generation process into a series of sequential steps. While effective for modest sizes, it loses its permutation invariance for larger graphs. Instead, we present a permutation invariant latent-variable generative model relying on graph embeddings to encode structure. Using tools from the random graph literature, our model is highly scalable to large graphs with likelihood evaluation and generation in O(|V | + |E|).