# Edge-based sequential graph generation with recurrent neural networks

arXiv: https://arxiv.org/abs/2002.00102 · HTML: https://mlsanity.com/paper/2002.00102/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 16 of 16)

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- [Flatten Graphs as Sequences: Transformers are Scalable Graph Generators](https://mlsanity.com/paper/2502.02216/) (2025)
- [GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation](https://mlsanity.com/paper/2107.08396/) (2021)
- [Overcoming Order in Autoregressive Graph Generation](https://mlsanity.com/paper/2402.03387/) (2024)
- [SCGG: A Deep Structure-Conditioned Graph Generative Model](https://mlsanity.com/paper/2209.09681/) (2022)
- [Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs](https://mlsanity.com/paper/2305.09705/) (2023)
- [Large Generative Graph Models](https://mlsanity.com/paper/2406.05109/) (2024)
- [Fast Graph Generation via Autoregressive Noisy Filtration Modeling](https://mlsanity.com/paper/2502.02415/) (2025)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Gransformer: Transformer-based Graph Generation](https://mlsanity.com/paper/2203.13655/) (2022)
- [Unsupervised Learning of Graph from Recipes](https://mlsanity.com/paper/2401.12088/) (2024)
- [Exploring Graph Representations of Logical Forms for Language Modeling](https://mlsanity.com/paper/2505.14523/) (2025)

## Abstract (arXiv)

Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions, while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task.