The paper by A.L. Barabási et al. explores the evolution of the co-authorship network of scientists, which is a prototype of complex evolving networks. The authors analyze data from mathematics and neuro-science journals published between 1991 and 1998 to understand the dynamic and structural mechanisms governing the network's evolution and topology. They find that the network is scale-free, with preferential attachment affecting both internal and external links. However, contrary to most model predictions, the average degree increases over time, and node separation decreases. The authors propose a simple model that captures the network's time evolution and use numerical simulations to investigate quantities that cannot be predicted analytically. The results highlight the importance of internal links in determining the observed scaling behavior and network topology. The study suggests that the properties of the co-authorship network are not unique and could be useful for studying other complex evolving networks, such as the World Wide Web and social networks.The paper by A.L. Barabási et al. explores the evolution of the co-authorship network of scientists, which is a prototype of complex evolving networks. The authors analyze data from mathematics and neuro-science journals published between 1991 and 1998 to understand the dynamic and structural mechanisms governing the network's evolution and topology. They find that the network is scale-free, with preferential attachment affecting both internal and external links. However, contrary to most model predictions, the average degree increases over time, and node separation decreases. The authors propose a simple model that captures the network's time evolution and use numerical simulations to investigate quantities that cannot be predicted analytically. The results highlight the importance of internal links in determining the observed scaling behavior and network topology. The study suggests that the properties of the co-authorship network are not unique and could be useful for studying other complex evolving networks, such as the World Wide Web and social networks.