The paper by Guimerà and Amaral introduces a methodology to extract and display information from complex networks, particularly focusing on metabolic networks. The authors propose a "cartographic representation" of these networks, which involves identifying functional modules and classifying nodes into universal roles based on their intra- and inter-module connections. This method helps in understanding the structure and function of complex biological networks, such as metabolic networks, by simplifying the network representation and highlighting key nodes and modules. Key findings include: 1. **Module Identification**: The authors use simulated annealing to identify functional modules in complex networks, achieving high accuracy even when a significant portion of a node's connections are outside its module. 2. **Node Classification**: Nodes are classified into seven universal roles based on their within-module degree and participation coefficient, which measures how links are distributed among different modules. 3. **Metabolic Networks Analysis**: The method is applied to metabolic networks of twelve organisms from three different super-kingdoms (bacteria, eukaryotes, and archaea). The results show that 80% of nodes are only connected within their respective modules, and nodes with different roles are affected by different evolutionary constraints. 4. **Conservation of Roles**: The authors find that low-degree metabolites that connect different modules are more conserved than hubs with mostly intra-module connections. This suggests that nodes with different roles may be under different evolutionary pressures, with structurally relevant roles being more conserved. The paper emphasizes the importance of considering the global role of nodes rather than just their local properties, providing a scale-specific method to extract knowledge from complex biological networks.The paper by Guimerà and Amaral introduces a methodology to extract and display information from complex networks, particularly focusing on metabolic networks. The authors propose a "cartographic representation" of these networks, which involves identifying functional modules and classifying nodes into universal roles based on their intra- and inter-module connections. This method helps in understanding the structure and function of complex biological networks, such as metabolic networks, by simplifying the network representation and highlighting key nodes and modules. Key findings include: 1. **Module Identification**: The authors use simulated annealing to identify functional modules in complex networks, achieving high accuracy even when a significant portion of a node's connections are outside its module. 2. **Node Classification**: Nodes are classified into seven universal roles based on their within-module degree and participation coefficient, which measures how links are distributed among different modules. 3. **Metabolic Networks Analysis**: The method is applied to metabolic networks of twelve organisms from three different super-kingdoms (bacteria, eukaryotes, and archaea). The results show that 80% of nodes are only connected within their respective modules, and nodes with different roles are affected by different evolutionary constraints. 4. **Conservation of Roles**: The authors find that low-degree metabolites that connect different modules are more conserved than hubs with mostly intra-module connections. This suggests that nodes with different roles may be under different evolutionary pressures, with structurally relevant roles being more conserved. The paper emphasizes the importance of considering the global role of nodes rather than just their local properties, providing a scale-specific method to extract knowledge from complex biological networks.