The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often simulation approaches involve selecting a suitable network generative model such as Erd\"os-R\'enyi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures, and the formation of real-world networks.

翻译：基于经验数据模拟真实网络的能力是跨科学领域（从流行病学到计算机科学）的一项重要任务。通常，模拟方法涉及选择合适的网络生成模型，例如Erd\"os-R\'enyi模型或小世界模型。然而，目前鲜有工具能够量化特定生成模型是否适合捕捉给定网络的结构或组织方式。我们利用可解释机器学习的进展，基于各种网络属性（包括主要特征及其交互作用），通过生成模型对模拟网络进行分类。我们的研究强调了特定网络特征及其交互作用在区分生成模型、理解复杂网络结构以及解析现实世界网络形成机制中的重要性。