The article "Discriminative topological features reveal biological network mechanisms" by Manuel Middendorf et al. presents a method to systematically assess which of a set of proposed network generation algorithms best describes a given biological network. The authors construct a mapping from the set of all graphs to a high-dimensional "word space," allowing for the classification of networks using support vector machines (SVMs). They apply this method to three real-world networks: the *E. coli* genetic network, the *S. cerevisiae* protein interaction network, and the *C. elegans* neuronal network. The results show that different duplication-mutation schemes are best for describing these networks, outperforming models like linear preferential attachment and small-world models. The study highlights the importance of systematic evaluation of network models and their predictability, providing a tool that can be useful for various scientific communities.The article "Discriminative topological features reveal biological network mechanisms" by Manuel Middendorf et al. presents a method to systematically assess which of a set of proposed network generation algorithms best describes a given biological network. The authors construct a mapping from the set of all graphs to a high-dimensional "word space," allowing for the classification of networks using support vector machines (SVMs). They apply this method to three real-world networks: the *E. coli* genetic network, the *S. cerevisiae* protein interaction network, and the *C. elegans* neuronal network. The results show that different duplication-mutation schemes are best for describing these networks, outperforming models like linear preferential attachment and small-world models. The study highlights the importance of systematic evaluation of network models and their predictability, providing a tool that can be useful for various scientific communities.