# Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation

arXiv: https://arxiv.org/abs/2505.07777 · HTML: https://mlsanity.com/paper/2505.07777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [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)
- [A Systematic Survey on Deep Generative Models for Graph Generation](https://mlsanity.com/paper/2007.06686/) (2020)
- [Deep Graph Generators: A Survey](https://mlsanity.com/paper/2012.15544/) (2020)
- [Disentangled Dynamic Graph Deep Generation](https://mlsanity.com/paper/2010.07276/) (2020)
- [Flow-based Network Traffic Generation using Generative Adversarial Networks](https://mlsanity.com/paper/1810.07795/) (2018)
- [A Framework for Large Scale Synthetic Graph Dataset Generation](https://mlsanity.com/paper/2210.01944/) (2022)

## Abstract (arXiv)

Obtaining real-world network datasets is often challenging because of privacy, security, and computational constraints. In the absence of such datasets, graph generative models become essential tools for creating synthetic datasets. In this paper, we introduce a novel machine learning model for generating high-fidelity synthetic network flow datasets that are representative of real-world networks. Our approach involves the generation of dynamic multigraphs using a stochastic Kronecker graph generator for structure generation and a tabular generative adversarial network for feature generation. We further employ an XGBoost (eXtreme Gradient Boosting) model for graph alignment, ensuring accurate overlay of features onto the generated graph structure. We evaluate our model using new metrics that assess both the accuracy and diversity of the synthetic graphs. Our results demonstrate improvements in accuracy over previous large-scale graph generation methods while maintaining similar efficiency. We also explore the trade-off between accuracy and diversity in synthetic graph dataset creation, a topic not extensively covered in related works. Our contributions include the synthesis and evaluation of large real-world netflow datasets and the definition of new metrics for evaluating synthetic graph generative models.