Brain networks are refined in the transition from childhood to adulthood. Our understanding of wiring mechanisms underlying development, however, remains very limited. Prior work has tested several predefined wiring models and suggested increased connectivity among nodes sharing similar neighbors in addition to cost minimization. While illuminating, this hypothesis-driven approach is prone to missing unforeseen network features and is biased by the researcher’s deductive thinking. Here we present a novel deep learning-based generative network model (Deep GNM) to infer developmental mechanisms in network formation in a data-informed fashion