# Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction

arXiv: https://arxiv.org/abs/1911.05469 · HTML: https://mlsanity.com/paper/1911.05469/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

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

- [A Survey on Deep Graph Generation: Methods and Applications](https://mlsanity.com/paper/2203.06714/) (2022)
- [Exploring the Potential of Large Language Models in Graph Generation](https://mlsanity.com/paper/2403.14358/) (2024)
- [CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation](https://mlsanity.com/paper/2110.03800/) (2021)
- [GraphRCG: Self-Conditioned Graph Generation](https://mlsanity.com/paper/2403.01071/) (2024)
- [DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform](https://mlsanity.com/paper/2504.06312/) (2025)
- [Gransformer: Transformer-based Graph Generation](https://mlsanity.com/paper/2203.13655/) (2022)
- [Generating Large Semi-Synthetic Graphs of Any Size](https://mlsanity.com/paper/2507.02166/) (2025)

## Abstract (arXiv)

Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs. Different types of graphs contain different network motifs, an example of which are triangles that often arise in social and biological networks. It is hence vital to capture these higher-order structures to simulate real-world networks accurately. We propose Multi-MotifGAN (MMGAN), a motif-targeted Generative Adversarial Network (GAN) that generalizes the benchmark NetGAN approach. The generalization consists of combining multiple biased random walks, each of which captures a different motif structure. MMGAN outperforms NetGAN at creating new graphs that accurately reflect the network motif statistics of input graphs such as Citeseer, Cora and Facebook.