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Papers by Stefano Boccaletti

Philosophical transactions of the Royal Society of London. Series B, Biological sciences, Jan 5, 2014

Many physical and biological systems can be studied using complex network theory, a new statistic... more Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can...

Chaos, 2013

We extend the concept of eigenvector centrality to multiplex networks, and introduce several alte... more We extend the concept of eigenvector centrality to multiplex networks, and introduce several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities. In addition, we rigorously show that, under reasonable conditions, such centrality measures exist and are unique. Computer experiments and simulations demonstrate that the proposed measures provide substantially different results when applied to the same multiplex structure, and highlight the non-trivial relationships between the different measures of centrality introduced.

Physical Biology, 2009

Modular organization is a special feature shared by many biological and social networks alike. It... more Modular organization is a special feature shared by many biological and social networks alike. It is a hallmark for systems exhibiting multitasking, in which individual tasks are performed by separated and yet coordinated functional groups. Understanding how networks of segregated modules develop to support coordinated multitasking functionalities is the main topic of the current study. Using simulations of biologically inspired neuronal networks during development, we study the formation of functional groups (cliques) and inter-neuronal synchronization. The results indicate that synchronization cliques first develop locally according to the explicit network topological organization. Later on, at intermediate connectivity levels, when networks have both local segregation and long-range integration, new synchronization cliques with distinctive properties are formed. In particular, by defining a new measure of synchronization centrality, we identify at these developmental stages dominant neurons whose functional centrality largely exceeds the topological one. These are generated mainly in a few dominant clusters that become the centers of the newly formed synchronization cliques. We show that by the local synchronization properties at the very early developmental stages, it is possible to predict with high accuracy which clusters will become dominant in later stages of network development.

Plos One, 2014

In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to exp... more In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro-and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations.

Plos One, 2011

Whether the balance between integration and segregation of information in the brain is damaged in... more Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.

Scientific Reports, 2012

We report on a generic procedure to steer (target) a network's dynamics towards a given, desired ... more We report on a generic procedure to steer (target) a network's dynamics towards a given, desired evolution. The problem is here tackled through a Master Stability Function approach, assessing the stability of the aimed dynamics, and through a selection of nodes to be targeted. We show that the degree of a node is a crucial element in this selection process, and that the targeting mechanism is most effective in heterogeneous scale-free architectures. This makes the proposed approach applicable to the large majority of natural and man-made networked systems. C omplex networks are mathematical objects with the ability to neatly encode relevant information on the irregular structure of interactions among coupled dynamical units, thus serving as useful models of largescale systems of biological, physical and social interest 1 . For a given coupling scheme, an issue of the utmost importance is how to make the network abandon its current time evolution (as defined by its equations of motion and initial condition) and approach a goal dynamics. Traditionally, this has been the subject of the theory of chaos control and targeting of dynamical systems, whose methods have laid the basis for a judicious manipulation of a nonlinear dynamics, cleverly directing it towards a desired one. The idea behind control 2 is that of stabilizing one of the infinite number of unstable orbits embedded in chaotic attractors by the application of small time-dependent perturbations. The targeting procedure 3-6 , instead, seeks to steer the dynamics of the system in the shortest possible time towards another trajectory fully compatible with the equations of motion of the system, but originating from a different initial condition.

European Physical Journal-Special Topics, 2013

We study the dynamics of the European Air Transport Network by using a multiplex network formalis... more We study the dynamics of the European Air Transport Network by using a multiplex network formalism. We will consider the set of flights of each airline as an interdependent network and we analyze the resilience of the system against random flight failures in the passenger's rescheduling problem. A comparison between the single-plex approach and the corresponding multiplex one is presented illustrating that the multiplexity strongly affects the robustness of the European Air Network. a

Physical Review E - PHYS REV E, 2006

A dynamical network (consisting of a time-evolving wiring of interactions among a group of random... more A dynamical network (consisting of a time-evolving wiring of interactions among a group of random walkers) is introduced to model the spread of an infectious disease in a population of mobile individuals. We investigate the main properties of this model, and show that peculiar features arise when individuals are allowed to perform long-distance jumps. Such peculiarities are captured and conveniently quantified by a series of appropriate parameters able to highlight the structural differences emerging in the networks when long-distance jumps are combined with local random walk processes.

Physical Review E, 2012

We introduce a concept of generalized synchronization, able to encompass the setting of collectiv... more We introduce a concept of generalized synchronization, able to encompass the setting of collective synchronized behavior for mutually coupled systems and networking systems featuring complex topologies in their connections. The onset of the synchronous regime is confirmed by the dependence of the system's Lyapunov exponents on the coupling parameter. The presence of a generalized synchronization regime is verified by means of the nearest neighbor method.