# GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features

arXiv: https://arxiv.org/abs/2201.11494 · HTML: https://mlsanity.com/paper/2201.11494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders](https://mlsanity.com/paper/1802.03480/) (2018)
- [Learning Deep Generative Models of Graphs](https://mlsanity.com/paper/1803.03324/) (2018)
- [Hierarchical Generation of Molecular Graphs using Structural Motifs](https://mlsanity.com/paper/2002.03230/) (2020)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [Multi-Objective De Novo Drug Design with Conditional Graph Generative Model](https://mlsanity.com/paper/1801.07299/) (2018)
- [A Systematic Survey on Deep Generative Models for Graph Generation](https://mlsanity.com/paper/2007.06686/) (2020)
- [GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation](https://mlsanity.com/paper/2001.08184/) (2020)
- [Deep Graph Generators: A Survey](https://mlsanity.com/paper/2012.15544/) (2020)
- [DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation](https://mlsanity.com/paper/1811.09766/) (2018)
- [The Anatomy of a Scientific Rumor](https://mlsanity.com/paper/1301.2952/) (2013)
- [Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing](https://mlsanity.com/paper/1809.00773/) (2018)
- [A Scalable Generative Graph Model with Community Structure](https://mlsanity.com/paper/1302.6636/) (2013)
- [Graph Generators: State of the Art and Open Challenges](https://mlsanity.com/paper/2001.07906/) (2020)
- [Disentangling Interpretable Generative Parameters of Random and Real-World Graphs](https://mlsanity.com/paper/1910.05639/) (2019)
- [A Tunable Model for Graph Generation Using LSTM and Conditional VAE](https://mlsanity.com/paper/2104.09304/) (2021)

## Built on by (most cited first, 1 of 1)

- [An Accurate Graph Generative Model with Tunable Features](https://mlsanity.com/paper/2309.01158/) (2023)

## Abstract (arXiv)

Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-world graphs has been attracting the attention of many researchers. Although several generative models that utilize modern machine learning technologies have been proposed, conditional generation of general graphs has been less explored in the field. In this paper, we propose a generative model that allows us to tune the value of a global-level structural feature as a condition. Our model, called GraphTune, makes it possible to tune the value of any structural feature of generated graphs using Long Short Term Memory (LSTM) and a Conditional Variational AutoEncoder (CVAE). We performed comparative evaluations of GraphTune and conventional models on a real graph dataset. The evaluations show that GraphTune makes it possible to more clearly tune the value of a global-level structural feature better than conventional models.