Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society [1–5]. Identifying the most efficient “spreaders” in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, there are plausible circum- stances where the best spreaders do not correspond to the best connected people or to the most central people (high betweenness centrality) [6–10]. Instead, we find: (i) The most efficient spreaders are those located within the core of the network as identified by the k -shell decomposition analysis [11–13]. (ii) When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extent of the spreading. Furthermore, we find that infections persist in the high k -shells of the network, even in the case where recovered individuals do not develop immunity. Our anal- ysis provides a plausible route for an optimal design of efficient dissemination strategies. Spreading is a ubiquitous process which describes many important activities in society [2–5]. The knowledge of the spreading pathways through the network of social interactions is crucial for developing efficient methods to either hinder spreading in the case of diseases, or accelerate spreading in the case of information dissemination. Indeed, people are connected according to the way they interact with each other in society and the large heterogeneity of the resulting network greatly determines the efficiency and speed of spreading. In the case of networks with a broad degree distribution (number of links per node) [6], it is believed that the most connected people (hubs) are the key players being responsible for the largest scale of the spreading process [6–8]. Furthermore, in the context of social network theory, the importance of a node for spreading is often associated with the betweenness centrality, a measure of how many shortest paths cross through this node, which is believed to determine who has more ‘interpersonal influence’ on others [9, 10]. Here we argue that the topology of the network organization plays an important role such that there are plausible circumstances under which the highly connected nodes or the highest betweenness nodes have little effect in the range of a given spreading process. For example, if a hub exists at the end of a branch at the periphery of a network, it will have a minimal impact in the spreading process through the core of the network, while a less connected 2