The paper "Identification of influential spreaders in complex networks" by Maksim Kitsak et al. explores the efficiency of information or disease spread in complex networks. The authors challenge the common belief that the most connected or central nodes are the best spreaders. Instead, they find that the most efficient spreaders are often located within the core of the network, as identified by the $k$-shell decomposition analysis. They demonstrate that when multiple spreaders are considered simultaneously, the distance between them becomes a crucial parameter for determining the extent of spreading. The study uses real-world networks, including social networks and contact networks, and applies the susceptible-infectious-recovered (SIR) and susceptible-infectious-susceptible (SIS) models to analyze spreading processes. The results show that nodes in high-$k$ shells are more effective spreaders, even if they are not highly connected, and that this is particularly true for infections where recovered individuals do not develop immunity. The authors conclude that the $k$-shell index is a better predictor of spreading influence than degree or betweenness centrality, and provide insights into optimal design strategies for efficient dissemination.The paper "Identification of influential spreaders in complex networks" by Maksim Kitsak et al. explores the efficiency of information or disease spread in complex networks. The authors challenge the common belief that the most connected or central nodes are the best spreaders. Instead, they find that the most efficient spreaders are often located within the core of the network, as identified by the $k$-shell decomposition analysis. They demonstrate that when multiple spreaders are considered simultaneously, the distance between them becomes a crucial parameter for determining the extent of spreading. The study uses real-world networks, including social networks and contact networks, and applies the susceptible-infectious-recovered (SIR) and susceptible-infectious-susceptible (SIS) models to analyze spreading processes. The results show that nodes in high-$k$ shells are more effective spreaders, even if they are not highly connected, and that this is particularly true for infections where recovered individuals do not develop immunity. The authors conclude that the $k$-shell index is a better predictor of spreading influence than degree or betweenness centrality, and provide insights into optimal design strategies for efficient dissemination.