# Labeled Graph Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1906.03220 · HTML: https://mlsanity.com/paper/1906.03220/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [Unconditional Scene Graph Generation](https://mlsanity.com/paper/2108.05884/) (2021)
- [Link Prediction with Non-Contrastive Learning](https://mlsanity.com/paper/2211.14394/) (2022)
- [Molecular Graph Generation via Geometric Scattering](https://mlsanity.com/paper/2110.06241/) (2021)
- [CARL-G: Clustering-Accelerated Representation Learning on Graphs](https://mlsanity.com/paper/2306.06936/) (2023)
- [GEMS: Scene Expansion using Generative Models of Graphs](https://mlsanity.com/paper/2207.03729/) (2022)
- [Size Matters: Large Graph Generation with HiGGs](https://mlsanity.com/paper/2306.11412/) (2023)
- [Overcoming Order in Autoregressive Graph Generation](https://mlsanity.com/paper/2402.03387/) (2024)
- [Generating a Doppelganger Graph: Resembling but Distinct](https://mlsanity.com/paper/2101.09593/) (2021)
- [Network Generation with Differential Privacy](https://mlsanity.com/paper/2111.09085/) (2021)
- [Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing](https://mlsanity.com/paper/2304.05137/) (2023)
- [Adaptive Edge Learning for Density-Aware Graph Generation](https://mlsanity.com/paper/2601.23052/) (2026)
- [Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach](https://mlsanity.com/paper/2605.29161/) (2026)

## Abstract (arXiv)

As a new approach to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation. This framework has also recently been applied to data with graph structures. We propose labeled-graph generative adversarial networks (LGGAN) to train deep generative models for graph-structured data with node labels. We test the approach on various types of graph datasets, such as collections of citation networks and protein graphs. Experiment results show that our model can generate diverse labeled graphs that match the structural characteristics of the training data and outperforms all alternative approaches in quality and generality. To further evaluate the quality of the generated graphs, we use them on a downstream task of graph classification, and the results show that LGGAN can faithfully capture the important aspects of the graph structure.