Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
This work addresses graph generation for applications in biology and social sciences, but it is incremental as it builds on existing methods to improve scalability and invariance.
The authors tackled the problem of generating large graphs while maintaining permutation invariance, which GraphRNN loses, by introducing a latent-variable model using graph embeddings, achieving scalability with likelihood evaluation and generation in O(|V| + |E|).
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 $O(|V | + |E|)$.