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.

翻译：随着关系数据的日益丰富，复杂系统的网络表示方法得到了越来越广泛的应用。这些模型逐渐发展，能够捕捉更细微的特性，例如关系的异质性，从而催生了多层网络的概念。然而，对这些结构进行分析和方法评估常常受限于大规模实证数据的稀缺。因此，图生成器常被用作一种替代方案，尽管这会引入系统性偏差。本文通过从真实系统中推断多层网络生成器mABCD的配置参数，解决了逆向生成器问题。我们的目标是找到能够使生成器产生作为原始结构数字孪生的合成网络的参数设置。我们提出了一种估计匹配配置并量化相关误差的方法。结果表明，这项任务并非易事，因为配置参数之间的强相互依赖性削弱了独立估计的有效性，反而更支持联合预测方法。