Maslov and Sneppen investigate the topology of protein interaction and regulatory networks in yeast, revealing that highly connected proteins (hubs) are less likely to connect to other highly connected proteins, while they are more likely to connect to lowly connected ones. This pattern reduces cross-talk between functional modules and enhances network robustness by isolating perturbations. The protein interaction network is scale-free, with connectivity following a power law. The regulatory network also shows similar correlation patterns. Both networks exhibit a decline in the average connectivity of neighbors as the node's own connectivity increases. This suggests a universal feature in molecular networks. The observed patterns are consistent with modularity and compartmentalization in cellular processes. The study also highlights the importance of considering experimental biases in network data. The results imply that molecular networks are both robust and specific, with functional modules organized around hubs. The findings have implications for understanding network robustness and the spread of perturbations. The study also shows that the correlation patterns observed in protein networks are similar to those in other complex systems like the internet.Maslov and Sneppen investigate the topology of protein interaction and regulatory networks in yeast, revealing that highly connected proteins (hubs) are less likely to connect to other highly connected proteins, while they are more likely to connect to lowly connected ones. This pattern reduces cross-talk between functional modules and enhances network robustness by isolating perturbations. The protein interaction network is scale-free, with connectivity following a power law. The regulatory network also shows similar correlation patterns. Both networks exhibit a decline in the average connectivity of neighbors as the node's own connectivity increases. This suggests a universal feature in molecular networks. The observed patterns are consistent with modularity and compartmentalization in cellular processes. The study also highlights the importance of considering experimental biases in network data. The results imply that molecular networks are both robust and specific, with functional modules organized around hubs. The findings have implications for understanding network robustness and the spread of perturbations. The study also shows that the correlation patterns observed in protein networks are similar to those in other complex systems like the internet.