Edmilson Roque

Problem solving ( e.g. , drug design, traffic engineering, software development) by task forces represents a substantial portion of the economy of developed countries. Here we use an agent-based model of cooperative problem-solving systems to study the influence of diversity on the performance of a task force. We assume that agents cooperate by exchanging information on their partial success and use that information to imitate the more successful agent in the system —the model. The agents differ only in their propensities to copy the model. We find that, for easy tasks, the optimal organization is a homogeneous system composed of agents with the highest possible copy propensities. For difficult tasks, we find that diversity can prevent the system from being trapped in sub-optimal solutions. However, when the system size is adjusted to maximize the performance the homogeneous systems outperform the heterogeneous systems, i.e. , for optimal performance, sameness should ...

Author(s): Ekkehard Ullner and Antonio Politi

Complex ensembles of multiple components are found in many biological systems, such as brains. Researchers model a population of oscillators acting like firing neurons to study the onset of irregular collective dynamics.

[Phys. Rev. X 6, 011015] Published Fri Feb 19, 2016

Author(s): Ginestra Bianconi and Christoph Rahmede

Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravi…

[Phys. Rev. E] Published Thu Feb 18, 2016

Author(s): Soumen K. Patra and Anandamohan Ghosh

Characterization of spatiotemporal dynamics of coupled oscillatory systems can be done by computing the Lyapunov exponents. We study the spatiotemporal dynamics of randomly coupled network of Kuramoto oscillators and find that the spectral statistics obtained from the Lyapunov exponent spectrum show…

[Phys. Rev. E] Published Wed Feb 17, 2016

Author(s): Kevin P. O’Keeffe

We consider the transient behavior of globally coupled systems of identical pulse coupled oscillators. Synchrony develops through an aggregation phenomenon, with clusters of synchronized oscillators forming and growing larger in time. Previous work derived expressions for these time dependent cluste…

[Phys. Rev. E] Published Wed Feb 17, 2016

Author(s): Silvio C. Ferreira, Renan S. Sander, and Romualdo Pastor-Satorras

We consider a general criterion to discern the nature of the threshold in epidemic models on scale-free (SF) networks. Comparing the epidemic lifespan of the nodes with largest degrees with the infection time between them, we propose a general dual scenario, in which the epidemic transition is eithe…

[Phys. Rev. E] Published Fri Feb 19, 2016

Author(s): Yerali Gandica and Silvia Chiacchiera

We study F coupled q-state Potts models in a two-dimensional square lattice. The interaction between the different layers is attractive, to favour a simultaneous alignment in all of them, and its strength is fixed. The nature of the phase transition for zero field is numerically determined for F=2,3…

[Phys. Rev. E] Published Wed Feb 17, 2016

Author(s): Aming Li, Mark Broom, Jinming Du, and Long Wang

The evolution of populations is influenced by many factors, and the simple classical models have been developed in a number of important ways. Both population structure and multiplayer interactions have been shown to significantly affect the evolution of important properties, such as the level of co…

[Phys. Rev. E 93, 022407] Published Thu Feb 18, 2016

Heterogeneity induces rhythms of weakly coupled circadian neurons

Scientific Reports, Published online: 22 February 2016; doi:10.1038/srep21412

We consider a general criterion to discern the nature of the threshold in epidemic models on scale-free (SF) networks. Comparing the epidemic lifespan of the nodes with largest degrees with the infection time between them, we propose a general dual scenario, in which the epidemic transition is either ruled by a hub activation process, leading to a null threshold in the thermodynamic limit, or given by a collective activation process, corresponding to a standard phase transition with a finite threshold. We validate the proposed criterion applying it to different epidemic models, with waning immunity or heterogeneous infection rates in both synthetic and real SF networks. In particular, a waning immunity, irrespective of its strength, leads to collective activation with finite threshold in scale-free networks with large exponent, at odds with canonical theoretical approaches.

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Abstract

Biological organisms adapt to changes by processing informations from different sources, most notably from their ancestors and from their environment. We review an approach to quantify these informations by analyzing mathematical models of evolutionary dynamics and show how explicit results are obtained for a solvable subclass of these models. In several limits, the results coincide with those obtained in studies of information processing for communication, gambling or thermodynamics. In the most general case, however, information processing by biological populations shows unique features that motivate the analysis of specific models.

Large graphs can be found in a wide array of scientific fields ranging from sociology and biology to scientometrics and computer science. Their analysis is by no means a trivial task due to their sheer size and complex structure. Such structure encompasses features so diverse as diameter shrinking, power law degree distribution and self similarity, edge interdependence, and communities. When the adjacency matrix of a graph is considered, then new, spectral properties arise such as primary eigenvalue component decay function, eigenvalue decay function, eigenvalue sign alternation around zero, and spectral gap. Graph mining is the scientific field which attempts to extract information and knowledge from graphs through their structural and spectral properties. Graph modeling is the associated field of generating synthetic graphs with properties similar to those of real graphs in order to simulate the latter. Such simulations may be desirable because of privacy concerns, cost, or lack of access to real data. Pivotal to simulation are low- and high-level software packages offering graph analysis and visualization capabilities. This survey outlines the most important structural and spectral graph properties, a considerable number of graph models, as well the most common graph mining and graph learning tools.

Following the financial crisis of 2007-2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the details of how financial institutions interact typically play a decisive role, and a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilise the financial system, i.e. market integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the details of how institutions interact, showing that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details.

We propose a new three-dimensional map that demonstrates the two- and three-frequency quasi-periodicity. For this map all basic quasi-periodic bifurcations are possible. The study was realized by using method of Lyapunov charts completed by plots of Lyapunov exponents, phase portraits and bifurcation trees illustrating the quasi-periodic bifurcations. The features of the three-parameter structure associated with quasi-periodic Hopf bifurcation are discussed. The comparison with non-autonomous model is carried out.

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In this letter we explore how recurrence quantifier, the determinism ($\Delta$), can reveal stationarity breaking and coupling between physical systems. We demonstrate that it is possible to detect small variations in a dynamical system based only on temporal signal displayed by another system coupled to it. To introduce basic ideas, we consider a well known dynamical system composed of two master-slave coupled Lorenz oscillators. We start evidencing that due to the sensitivity of $\Delta$ computed from temporal time series of slave oscillator, its is possible to detect the stationary breaking imposed in the master oscillator. As a second example, the method is carried out in a real physiological data acquired from accelerometer sensors ($\mathrm{A_{cc}}$) and used to detect micro arousal phenomenology (described by a sharp burst in $\mathrm{A_{cc}}$ signal) during sleep periods in mice. Moreover, we show for the first time that making use of recurrence quantifier it is possible to infer a coupling between electric signals from hippocampus and "locomotor brain areas" of mice, based only on non invasive data from $\mathrm{A_{cc}}$. Our results suggest new possibilities of analysis of coupled systems making use of accessible time series. Our second example supports an interpretation of an internal coupling detectable as a stationarity breaking in $\mathrm{A_{cc}}$ that occurs some seconds before micro arousal processes during sleep periods in rodents, contributing to the idea that micro arousals are elements of sleep taking part in the regulation of sleep process. Such a characterization of micro arousals can improve our knowledge about sleep fostering tools of sleep diagnose and pharmacology research for mammals in general.

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Chaos synchronization may arise in networks of nonlinear units with delayed couplings. We study complete and sublattice synchronization generated by resonance of two large time delays with a specific ratio. As it is known for single delay networks, the number of synchronized sublattices is determined by the Greatest Common Divisor (GCD) of the network loops lengths. We demonstrate analytically the GCD condition in networks of iterated Bernouilli maps with multiple delay times and complement our analytic results by numerical phase diagrams, providing parameter regions showing complete and sublattice synchronization by resonance for Tent and Bernouilli maps. We compare networks with the same GCD with single and multiple delays, and we investigate the sensitivity of the correlation to a detuning between the delays in a network of coupled Stuart-Landau oscillators. Moreover, the GCD condition also allows to detect time delay resonances leading to high correlations in non-synchronizable networks. Specifically, GCD-induced resonances are observed both in a chaotic asymmetric network and in doubly connected rings of delay-coupled noisy linear oscillators.

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Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises. Authors: Stefano Battiston, J. Doyne Farmer, Andreas Flache, Diego Garlaschelli, Andrew G. Haldane, Hans Heesterbeek, Cars Hommes, Carlo Jaeger, Robert May, Marten Scheffer

Author(s): Henry W. Lin and Abraham Loeb

The spatial distribution of people exhibits clustering across a wide range of scales, from household ( ~ 10-2 \textkm) to continental ( ~ 104 \textkm) scales. Empirical data indicates simple power-law scalings for the size distribution of cities (known as Zipf's law) and the population den…

[Phys. Rev. E] Published Fri Feb 12, 2016

Chimera states—curious symmetry-broken states in systems of identical coupled oscillators—typically occur only for certain initial conditions. Here we analyze their basins of attraction in a simple system comprised of two populations. Using perturbative analysis and numerical simulation we evaluate asymptotic states and associated destination maps, and demonstrate that basins form a complex twisting structure in phase space. Understanding the basins’ precise nature may help in the development of control methods to switch between chimera patterns, with possible technological and neural system applications.

Author(s): Shantanav Chakraborty, Leonardo Novo, Andris Ambainis, and Yasser Omar

The problem of finding a marked node in a graph can be solved by the spatial search algorithm based on continuous-time quantum walks (CTQW). However, this algorithm is known to run in optimal time only for a handful of graphs. In this work, we prove that for Erdös-Renyi random graphs, i.e.graph…

[Phys. Rev. Lett.] Published Tue Feb 16, 2016

Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of real-world temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena.

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The problem of community detection is important as it helps in understanding the spread of information in a social network. All real complex networks have an inbuilt structure which captures and characterizes the network dynamics between its nodes. Linkages are more likely to form between similar nodes, leading to the formation of some community structure which characterizes the network dynamic. The more friends they have in common, the more the influence that each person can exercise on the other.

We propose a disjoint community detection algorithm, $\textit{NashDisjoint}$ that detects disjoint communities in any given network. We evaluate the algorithm $\textit{NashDisjoint}$ on the standard LFR benchmarks, and we find that our algorithm works at least as good as that of the state of the art algorithms for the mixing factors less than 0.55 in all the cases. We propose an overlapping community detection algorithm $\textit{NashOverlap}$ to detect the overlapping communities in any given network. We evaluate the algorithm $\textit{NashOverlap}$ on the standard LFR benchmarks and we find that our algorithm works far better than the state of the art algorithms in around 152 different scenarios, generated by varying the number of nodes, mixing factor and overlapping membership.

We run our algorithm $\textit{NashOverlap}$ on DBLP dataset to detect the large collaboration groups and found very interesting results. Also, these results of our algorithm on DBLP collaboration network are compared with the results of the $\textit{COPRA}$ algorithm and $\textit{OSLOM}$.

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This paper proposes a crowd dynamic macroscopic model grounded on microscopic phenomenological observations which are upscaled by means of a formal mathematical procedure. The actual applicability of the model to real world problems is tested by considering the pedestrian traffic along footbridges, of interest for Structural and Transportation Engineering. The genuinely macroscopic quantitative description of the crowd flow directly matches the engineering need of bulk results. However, three issues beyond the sole modelling are of primary importance: the pedestrian inflow conditions, the numerical approximation of the equations for non trivial footbridge geometries, and the calibration of the free parameters of the model on the basis of in situ measurements currently available. These issues are discussed and a solution strategy is proposed.

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In this chapter we give an overview of the application of complex network theory to quantify some properties of language. Our study is based on two fables in Ukrainian, Mykyta the Fox and Abu-Kasym's slippers. It consists of two parts: the analysis of frequency-rank distributions of words and the application of complex-network theory. The first part shows that the text sizes are sufficiently large to observe statistical properties. This supports their selection for the analysis of typical properties of the language networks in the second part of the chapter. In describing language as a complex network, while words are usually associated with nodes, there is more variability in the choice of links and different representations result in different networks. Here, we examine a number of such representations of the language network and perform a comparative analysis of their characteristics. Our results suggest that, irrespective of link representation, the Ukrainian language network used in the selected fables is a strongly correlated, scale-free, small world. We discuss how such empirical approaches may help form a useful basis for a theoretical description of language evolution and how they may be used in analyses of other textual narratives.

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Physics: Post-PhD job stability

Nature 530, 7590 (2016). doi:10.1038/nj7590-373b

Fewer than one-third of physics PhD graduates take permanent jobs, and that is a record high.

Universal resilience patterns in complex networks

Nature 530, 7590 (2016). doi:10.1038/nature16948

Authors: Jianxi Gao, Baruch Barzel & Albert-László Barabási

Resilience, a system’s ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems. Despite widespread consequences for human health, the economy and the environment, events leading to loss of resilience—from cascading failures in technological systems to mass extinctions in ecological networks—are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system’s resilience. The proposed analytical framework allows us systematically to separate the roles of the system’s dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.

In this paper we show how the Parameter Switching algorithm, utilized initially to approximate attractors of a general class of nonlinear dynamical systems, can be utilized also as a synchronization-induced method. Two illustrative examples are considered: the Lorenz system and the Rabinovich-Fabrikant system.

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Author(s): Oliver Mülken, Maxim Dolgushev, and Mircea Galiceanu

We study the transport efficiency of excitations on complex quantum networks with loops. For this we consider sequentially growing networks with different topologies of the sequential subgraphs. This can lead either to a universal complete breakdown of transport for complete-graph-like sequential su…

[Phys. Rev. E 93, 022304] Published Tue Feb 16, 2016

In this paper we present an influence of discontinuous coupling on the dynamics of multistable systems. Our model consists of two periodically forced oscillators that can interact via soft impacts. The controlling parameters are the distance between the oscillators and the difference in the phase of the harmonic excitation. When the distance is large two systems do not collide and a number of different possible solutions can be observed in both of them. When decreasing of the distance, one can observe some transient impacts and then the systems settle down on non-impacting attractor. It is shown that with the properly chosen distance and difference in the phase of the harmonic excitation, the number of possible solutions of the coupled systems can be reduced. The proposed method is robust and applicable in many different systems.