This paper presents a method for extracting and displaying information from complex networks, particularly metabolic networks. The method identifies functional modules within networks and classifies nodes into universal roles based on their intra- and inter-module connections. This approach provides a "cartographic representation" of complex networks, enabling the identification of key functional components and their roles. Metabolic networks, which represent biochemical reactions in organisms, are analyzed using this method. The study examines the metabolic networks of twelve organisms across three super-kingdoms. The results show that 80% of nodes are connected only within their modules, and nodes with different roles are subject to different evolutionary constraints. Low-degree metabolites that connect different modules are more conserved than hubs with links mostly within a single module. The method identifies seven universal roles for nodes based on their within-module degree and participation coefficient. Non-hub nodes are classified into four roles, while hub nodes are classified into three roles. These roles help in understanding the functional organization of metabolic networks. The study also finds that non-hub connectors are more conserved than provincial hubs, suggesting that nodes with different roles are under different evolutionary pressures. The results highlight the importance of considering the global role of nodes in a network rather than just their degree. The method is applied to metabolic networks of twelve organisms, revealing the functional organization of these networks and the conservation of certain metabolites across species. The findings suggest that the global role of nodes is a better indicator of their importance than their degree. The study also highlights the need for further research on adapting module-detection algorithms to networks with hierarchical structures.This paper presents a method for extracting and displaying information from complex networks, particularly metabolic networks. The method identifies functional modules within networks and classifies nodes into universal roles based on their intra- and inter-module connections. This approach provides a "cartographic representation" of complex networks, enabling the identification of key functional components and their roles. Metabolic networks, which represent biochemical reactions in organisms, are analyzed using this method. The study examines the metabolic networks of twelve organisms across three super-kingdoms. The results show that 80% of nodes are connected only within their modules, and nodes with different roles are subject to different evolutionary constraints. Low-degree metabolites that connect different modules are more conserved than hubs with links mostly within a single module. The method identifies seven universal roles for nodes based on their within-module degree and participation coefficient. Non-hub nodes are classified into four roles, while hub nodes are classified into three roles. These roles help in understanding the functional organization of metabolic networks. The study also finds that non-hub connectors are more conserved than provincial hubs, suggesting that nodes with different roles are under different evolutionary pressures. The results highlight the importance of considering the global role of nodes in a network rather than just their degree. The method is applied to metabolic networks of twelve organisms, revealing the functional organization of these networks and the conservation of certain metabolites across species. The findings suggest that the global role of nodes is a better indicator of their importance than their degree. The study also highlights the need for further research on adapting module-detection algorithms to networks with hierarchical structures.