# Graph Generation with Variational Recurrent Neural Network

arXiv: https://arxiv.org/abs/1910.01743 · HTML: https://mlsanity.com/paper/1910.01743/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network](https://mlsanity.com/paper/2105.02565/) (2021)
- [TD-GEN: Graph Generation With Tree Decomposition](https://mlsanity.com/paper/2106.10656/) (2021)
- [Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs](https://mlsanity.com/paper/2105.12295/) (2021)
- [mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding](https://mlsanity.com/paper/2104.02433/) (2021)
- [CatVRNN: Generating Category Texts via Multi-task Learning](https://mlsanity.com/paper/2107.05219/) (2021)
- [Unsupervised Learning of Graph from Recipes](https://mlsanity.com/paper/2401.12088/) (2024)
- [Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling](https://mlsanity.com/paper/2410.11972/) (2024)

## Abstract (arXiv)

Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurrent Neural Network (GraphVRNN), a probabilistic autoregressive model for graph generation. Through modeling the latent variables of graph data, GraphVRNN can capture the joint distributions of graph structures and the underlying node attributes. We conduct experiments on the proposed GraphVRNN in both graph structure learning and attribute generation tasks. The evaluation results show that the variational component allows our network to model complicated distributions, as well as generate plausible structures and node attributes.