Showing papers by "Vito Latora published in 2014"

Structural measures for multiplex networks.

Abstract: Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular, we propose a series of measures to characterize the multiplexicity of the systems in terms of (i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, (ii) local properties such as the clustering coefficient and the transitivity, and (iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuinely multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications, and business relationships.

Network structure of multivariate time series

Abstract: Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range of tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Characteristic times of biased random walks on complex networks.

Abstract: We consider degree-biased random walkers whose probability to move from a node to one of its neighbors of degree $k$ is proportional to ${k}^{\ensuremath{\alpha}}$, where $\ensuremath{\alpha}$ is a tuning parameter. We study both numerically and analytically three types of characteristic times, namely (i) the time the walker needs to come back to the starting node, (ii) the time it takes to visit a given node for the first time, and (iii) the time it takes to visit all the nodes of the network. We consider a large data set of real-world networks and we show that the value of $\ensuremath{\alpha}$ which minimizes the three characteristic times differs from the value ${\ensuremath{\alpha}}_{\mathrm{min}}=\ensuremath{-}1$ analytically found for uncorrelated networks in the mean-field approximation. In addition to this, we found that assortative networks have preferentially a value of ${\ensuremath{\alpha}}_{\mathrm{min}}$ in the range $[\ensuremath{-}1,\ensuremath{-}0.5]$, while disassortative networks have ${\ensuremath{\alpha}}_{\mathrm{min}}$ in the range $[\ensuremath{-}0.5,0]$. We derive an analytical relation between the degree correlation exponent $\ensuremath{ u}$ and the optimal bias value ${\ensuremath{\alpha}}_{\mathrm{min}}$, which works well for real-world assortative networks. When only local information is available, degree-biased random walks can guarantee smaller characteristic times than the classical unbiased random walks by means of an appropriate tuning of the motion bias.

Assessment of urban ecosystem resilience through hybrid social-physical complex networks

Abstract: One of the most important tasks of urban and hazard planning is to mitigate the damages and minimize the costs of the recovery process after catastrophic events. In this context, the capability of urban systems and communities to recover from disasters is referred to as resilience. Despite the problem of resilience quantification having received a lot of attention, a mathematical definition of the resilience of an urban community, which takes into account the social aspects of an urban environment, has not yet been identified. In this article, we provide and test a methodology for the assessment of urban resilience to catastrophic events which aims at bridging the gap between the engineering and the ecosystem approaches to resilience. We propose to model an urban system by means of different hybrid social–physical complex networks, obtained by enriching the urban street network with additional information about the social and physical constituents of a city, namely citizens, residential buildings, and services. Then, we introduce a class of efficiency measures on these hybrid networks, inspired by the definition of global efficiency given in complex network theory, and we show that these measures can be effectively used to quantify the resilience of an urban system, by comparing their respective values before and after a catastrophic event and during the reconstruction process. As a case study, we consider simulated earthquakes in the city of Acerra, Italy, and we use these efficiency measures to compare the ability of different reconstruction strategies in restoring the original performance of the urban system.

Evolutionary dynamics of time-resolved social interactions.

Abstract: Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior

Nonlinear growth and condensation in multiplex networks.

Abstract: Different types of interactions coexist and coevolve to shape the structure and function of a multiplex network. We propose here a general class of growth models in which the various layers of a multiplex network coevolve through a set of nonlinear preferential attachment rules. We show, both numerically and analytically, that by tuning the level of nonlinearity these models allow us to reproduce either homogeneous or heterogeneous degree distributions, together with positive or negative degree correlations across layers. In particular, we derive the condition for the appearance of a condensed state in which one node in each layer attracts an extensive fraction of all the edges.

Characteristic exponents of complex networks

Abstract: We present a novel way to characterize the structure of complex networks by studying the statistical properties of the trajectories of random walks over them. We consider time series corresponding to different properties of the nodes visited by the walkers. We show that the analysis of the fluctuations of these time series allows to define a set of characteristic exponents which capture the local and global organization of a network. This approach provides a way of solving two classical problems in network science, namely the systematic classification of networks, and the identification of the salient properties of growing networks. The results contribute to the construction of a unifying framework for the investigation of the structure and dynamics of complex systems.

Nonparametric resampling of random walks for spectral network clustering.

Abstract: Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.

Evolutionary Dynamics of Time-Resolved Social Interactions

Abstract: Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals towards selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics.Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.

Hybrid recommendation methods in complex networks

Abstract: We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we compare their performance with several recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow to attain an improvement of performances of up to 20\% with respect to existing non-parametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we studied how an increasing presence of random links in the network affects the recommendation scores, and we found that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.