Researchers are studying the growth and structure of real-world networks, including stream and tidal networks, using geometric branching growth models. The studies suggest that the design of these networks may be influenced by factors such as surface optimization, climate, and topography. The use of techniques like geometric renormalization and multidimensional hyperbolic embedding is helping to uncover patterns and self-similarity in these complex networks.