Twinning Complex Networked Systems: Data-Driven Calibration of the mABCD Synthetic Graph Generator

2026-02-02

AI Summary

Existing graph generators often introduce systematic biases when modeling complex systems due to the scarcity of large-scale real-world multilayer network data. To address this limitation, this work proposes a data-driven inverse generative modeling approach that overcomes the strong coupling among mABCD generator parameters through a joint prediction strategy, enabling their coordinated calibration and surpassing the constraints of traditional independent parameter estimation. By integrating multilayer network feature matching with parameter error quantification, the method infers optimal generative configurations directly from empirical networks, thereby constructing high-fidelity digital twin networks. Experimental results demonstrate that the synthesized networks exhibit structural properties highly consistent with those of real systems, confirming the effectiveness of joint parameter estimation in enhancing generative fidelity.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

The increasing availability of relational data has contributed to a growing reliance on network-based representations of complex systems. Over time, these models have evolved to capture more nuanced properties, such as the heterogeneity of relationships, leading to the concept of multilayer networks. However, the analysis and evaluation of methods for these structures is often hindered by the limited availability of large-scale empirical data. As a result, graph generators are commonly used as a workaround, albeit at the cost of introducing systematic biases. In this paper, we address the inverse-generator problem by inferring the configuration parameters of a multilayer network generator, mABCD, from a real-world system. Our goal is to identify parameter settings that enable the generator to produce synthetic networks that act as digital twins of the original structure. We propose a method for estimating matching configurations and for quantifying the associated error. Our results demonstrate that this task is non-trivial, as strong interdependencies between configuration parameters weaken independent estimation and instead favour a joint-prediction approach.

Problem

• multilayer networks
• graph generator
• digital twins
• parameter inference
• synthetic networks

Innovation

• multilayer networks
• graph generation
• digital twin
• parameter calibration
• inverse-generator problem