Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation

2025-05-12

AI Summary

Real-world network flow data is often inaccessible due to privacy, security, and computational constraints. Method: This paper proposes a high-fidelity, diversity-controllable dynamic multigraph synthesis framework. Its core innovation lies in the first joint optimization of structural accuracy and attribute diversity. It employs a decoupled modeling strategy: Kronecker graph generation for topology, Tabular GANs for node/edge attribute synthesis, and an XGBoost-driven graph alignment mechanism to coordinate structural and attribute optimization. Customized evaluation metrics are designed to quantify synthesis quality. Results: Experiments on large-scale NetFlow data demonstrate that the method significantly outperforms existing graph generation techniques, achieving superior trade-offs among fidelity, diversity, and computational efficiency. Multiple validated synthetic datasets—capable of supporting privacy-sensitive modeling tasks—are successfully generated and empirically verified.

Technology Category

Natural Language Processing: GenerationMachine Learning: Graph-based Machine LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSocial Networks and Social Media: Generative AI / large language models and their impact on social systemsWeb Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Generating synthetic network flow datasets overcoming real-world data constraints
• Combining stochastic Kronecker graphs and GANs for dynamic multigraph generation
• Balancing accuracy and diversity in synthetic graphs using novel metrics

Innovation

• Dynamic multigraphs via stochastic Kronecker generator
• Feature generation using tabular GANs
• Graph alignment with XGBoost for structure-feature overlay