Can simpler rules naturally produce the complex networks we observe?

Standard network models assume independent connections and use Shannon entropy—arguably an arbitrary choice. Switching to a broader class of entropies that make fewer assumptions produces networks with phase transitions, clustering, and sparse-yet-interconnected triangles, mimicking real networks without adding extra constraints. This suggests complex network structure may emerge naturally from maximally neutral statistics.