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 towards optimizing the use of available resources and ensuring the more efficient spread of information. Here we show that, in contrast to common belief, there are plausible circumstances where the best spreaders do not correspond to the most highly connected or the most central people 6-10 . Instead, we find that the most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis [11][12][13] , and that when multiple spreaders are considered simultaneously the distance between them becomes the crucial parameter that determines the extent of the spreading. Furthermore, we show that infections persist in the high-k shells of the network in the case where recovered individuals do not develop immunity. Our analysis should provide a route for an optimal design of efficient dissemination strategies.Spreading is a ubiquitous process, which describes many important activities in society [2][3][4][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 one another 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][7][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 on 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, whereas a less connected person who is strategically placed in the core of the network will have a significant effect that leads to dissemination through a large fraction of the population. To identify the core and the periphery of the network we use the k-shell (also called k-core) decomposition of the network [11][12][13][14] . Examining this quantity in a number of real networks enables us to identify the best individual spreaders in the network when th...

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.

Covering a network with the minimum possible number of boxes can reveal interesting features for the network structure, especially in terms of self-similar or fractal characteristics. Considerable attention has been recently devoted to this problem, with the finding that many real networks are self-similar fractals. Here we present, compare and study in detail a number of algorithms that we have used in previous papers towards this goal. We show that this problem can be mapped to the well-known graph coloring problem and then we simply can apply well-established algorithms. This seems to be the most efficient method, but we also present two other algorithms based on burning which provide a number of other benefits. We argue that the presented algorithms provide a solution close to optimal and that another algorithm that can significantly improve this result in an efficient way does not exist. We offer to anyone that finds such a method to cover his/her expenses for a 1-week trip to our lab in New York (details in http://jamlab.org).

We study tolerance and topology of random scale-free networks under attack and defense strategies that depend on the degree k of the nodes. This situation occurs, for example, when the robustness of a node depends on its degree or in an intentional attack with insufficient knowledge of the network. We determine, for all strategies, the critical fraction p(c) of nodes that must be removed for disintegrating the network. We find that, for an intentional attack, little knowledge of the well-connected sites is sufficient to strongly reduce p(c). At criticality, the topology of the network depends on the removal strategy, implying that different strategies may lead to different kinds of percolation transitions.

The present paper deals with the photophysical properties of columnar liquid crystals formed by hexakis-(alky1oxy)triphenylenes. Absorption and fluorescence spectra of solutions are analyzed on the basis of quantum chemical calculations performed by the CS-INDO-CI (conformations spectra-intermediate neglect of differential overlap-configuration interaction) m e t h d . the absorption maximum is due to the SO -Sq transition while fluorescence originates from the weak SO -S1 transition. In columnar aggregates, the former transition corresponds to delocalized excited states while the latter corresponds to localized ones; calculation of intermolecular interactions shows that, at the temperature domain of the mesophases, all the molecules have the same excitation energy and, therefore, no spectral diffusion of the fluorescence is expected, in agreement with the time-resolved emission spectra. Excitation transfer is investigated by studying the fluorescence decays of mesophases doped with energy traps. Their analysis is made by means of Monte Carlo simulations considering both intracolumnar and intercolumnar jumps and using four different models for the distance dependence of the hopping probability. The best description is obtained with a model based on the extended dipole approximation and taking into account molecular orientation.