The paper by Sergei Maslov and Kim Sneppen explores the topological properties of molecular networks in living cells, focusing on both interaction and regulatory networks. They analyze the correlations between the connectivities of interacting nodes in these networks and compare them to a null model where all links are randomly rewired. The study reveals that links between highly connected proteins are systematically suppressed, while those between highly connected and low-connected proteins are favored. This effect reduces cross-talk between different functional modules and enhances the robustness of the network by localizing the effects of deleterious perturbations. The authors use data from the yeast *Saccharomyces cerevisiae* to examine the interaction and transcription regulatory networks. They find that the interaction network is scale-free, with a power-law distribution of connectivities, and that the regulatory network is directed. The correlation patterns in both networks are similar, with a tendency for highly connected nodes to associate with nodes of low connectivity and a reduced likelihood of direct links between hub nodes. The suppression of connections between hub nodes and their neighbors suggests a modular organization of cellular processes, where functional modules are centered around individual hubs. This modular structure also helps in reducing the propagation of deleterious perturbations, making the network more robust. The authors conclude that the observed correlation patterns are likely universal features of molecular networks in living cells, enhancing the specificity and stability of these networks.The paper by Sergei Maslov and Kim Sneppen explores the topological properties of molecular networks in living cells, focusing on both interaction and regulatory networks. They analyze the correlations between the connectivities of interacting nodes in these networks and compare them to a null model where all links are randomly rewired. The study reveals that links between highly connected proteins are systematically suppressed, while those between highly connected and low-connected proteins are favored. This effect reduces cross-talk between different functional modules and enhances the robustness of the network by localizing the effects of deleterious perturbations. The authors use data from the yeast *Saccharomyces cerevisiae* to examine the interaction and transcription regulatory networks. They find that the interaction network is scale-free, with a power-law distribution of connectivities, and that the regulatory network is directed. The correlation patterns in both networks are similar, with a tendency for highly connected nodes to associate with nodes of low connectivity and a reduced likelihood of direct links between hub nodes. The suppression of connections between hub nodes and their neighbors suggests a modular organization of cellular processes, where functional modules are centered around individual hubs. This modular structure also helps in reducing the propagation of deleterious perturbations, making the network more robust. The authors conclude that the observed correlation patterns are likely universal features of molecular networks in living cells, enhancing the specificity and stability of these networks.