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

arXiv: https://arxiv.org/abs/2602.02044 · HTML: https://mlsanity.com/paper/2602.02044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Network Geometry](https://mlsanity.com/paper/2001.03241/) (2020)
- [Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure](https://mlsanity.com/paper/2002.00843/) (2020)
- [Modularity of the ABCD Random Graph Model with Community Structure](https://mlsanity.com/paper/2203.01480/) (2022)
- [Hypergraph Artificial Benchmark for Community Detection (h-ABCD)](https://mlsanity.com/paper/2210.15009/) (2022)
- [Properties and Performance of the ABCDe Random Graph Model with Community Structure](https://mlsanity.com/paper/2203.14899/) (2022)
- [Artificial Benchmark for Community Detection with Outliers (ABCD+o)](https://mlsanity.com/paper/2301.05749/) (2023)
- [The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+o2)](https://mlsanity.com/paper/2506.05486/) (2025)
- [A Method for Group Extraction and Analysis in Multilayer Social Networks](https://mlsanity.com/paper/1612.02377/) (2016)
- [Self-similarity of Communities of the ABCD Model](https://mlsanity.com/paper/2312.00238/) (2023)
- [Network Embedding Exploration Tool (NEExT)](https://mlsanity.com/paper/2503.15853/) (2025)
- [Multilayer Artificial Benchmark for Community Detection (mABCD)](https://mlsanity.com/paper/2507.10795/) (2025)

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

- [Graph Data Augmentation via Contrastive Generator Inversion (textttDCBA)](https://mlsanity.com/paper/2610.05653/) (2026)

## Abstract (arXiv)

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.