Research on geometric renormalization of weighted networks has been reported, with studies examining its application to complex networks, including the human connectome. The technique appears to be used to analyze and understand the structure and properties of these networks at multiple scales. Various approaches, including laplacian renormalization group and machine learning, are being explored to uncover the nature of weights and dimensionality in real complex networks.