  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.
