Author(s): Zheng Xie and Tim Rogers
We introduce and analyze a class of growing geometric random graphs that are invariant under rescaling of space and time. Directed connections between nodes are drawn according to influence zones that depend on node position in space and time, mimicking the heterogeneity and increased specialization…
[Phys. Rev. E 93, 032310] Published Wed Mar 09, 2016
We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the SIS and SIR dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasi-stationary state method we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: if the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we verified the barrier effect, i.e., for three-layer configuration, when the layer with the largest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems opening new possibilities for the study of spreading processes.
We consider a system of urns of Polya-type, with balls of two colors; the reinforcement of each urn depends both on the content of the same urn and on the average content of all urns. We show that the urns synchronize almost surely, in the sense that the fraction of balls of a given color converges almost surely, as the time goes to infinity, to the same limit for all urns. A normal approximation for a large number of urns is also obtained.
Stability assessment methods for dynamical systems have recently been complemented by basin stability and derived measures, i.e. probabilistic statements whether systems remain in a basin of attraction given a distribution of perturbations. This requires numerical estimation via Monte-Carlo sampling and integration of differential equations. Here, we analyze the applicability of basin stability to systems with basin geometries challenging for this numerical method, having fractal basin boundaries and riddled or intermingled basins of attraction. We find that numerical basin stability estimation is still meaningful for fractal boundaries but reaches its limits for riddled basins with holes.
Author(s): Leonhard Lücken, Oleksandr V. Popovych, Peter A. Tass, and Serhiy Yanchuk
The authors address the plasticity of the coupling between neurons subjected to stochastic excitations. By reducing the problem to a system of two neurons, they show the onset of new states that do not appear in deterministic models. Interestingly, they find that noise could act to stabilize the coupling and enhance synchronization.
[Phys. Rev. E 93, 032210] Published Tue Mar 08, 2016
Author(s): Yanjun Lei, Xin Jiang, Quantong Guo, Yifang Ma, Meng Li, and Zhiming Zheng
The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreadin…
[Phys. Rev. E 93, 032308] Published Tue Mar 08, 2016
Author(s): Filippo Radicchi and Claudio Castellano
Theoretical attempts proposed so far to describe ordinary percolation processes on real-world networks rely on the locally tree-like ansatz. Such an approximation, however, holds only to a limited extent, as real graphs are often characterized by high frequencies of short loops. We present here a th…[Phys. Rev. E] Published Thu Mar 03, 2016
Author(s): S. M. Oh, S. -W. Son, and B. Kahng
Recent extensive studies of the explosive percolation (EP) model revealed that the EP transition is second-order with an extremely small value of the critical exponent b associated with the order parameter. This result was obtained from static networks, in which the number of nodes in the system rem…[Phys. Rev. E] Published Thu Mar 03, 2016
Author(s): H. -H. Yoo and D. -S. Lee
Synchronizing individual activities is essential for the stable functioning of diverse complex systems. Understanding the relation between dynamic fluctuations and the connection topology of substrates is therefore important, but it remains restricted to regular lattices. Here we investigate the flu…[Phys. Rev. E] Published Fri Mar 04, 2016
Author(s): Kay Jörg Wiese
renormalized field theories there are in general one or few fixed points which are accessible by the renormalization-group flow. They can be identified from the fixed-point equations. Exceptionally, an infinite family of fixed points exists, parameterized by a scaling exponent z, itself function of …[Phys. Rev. E] Published Fri Mar 04, 2016
Author(s): Heman Shakeri, Nathan Albin, Faryad Darabi Sahneh, Pietro Poggi-Corradini, and Caterina Scoglio
Algebraic connectivity, the second eigenvalue of the Laplacian matrix, is a measure of node and link connectivity on networks. When studying interconnected networks it is useful to consider a multiplex model, where the component networks operate together with inter-layer links among them. In order t…[Phys. Rev. E] Published Mon Mar 07, 2016
We consider recurrent contagious processes on a time-varying network. As a control procedure to mitigate the epidemic, we propose an adaptive rewiring mechanism for temporary isolation of infected nodes upon their detection. As a case study, we investigate the network of pig trade in Germany. Based on extensive numerical simulations for a wide range of parameters, we demonstrate that the adaptation mechanism leads to a significant extension of the parameter range, for which most of the index nodes (origins of the epidemic) lead to vanishing epidemics. Furthermore the performance of adaptation is very heterogeneous with respect to the index node. We quantify the success of the proposed adaptation scheme in dependence on the infectious period and the detection time. To support our findings we propose a mean-field analytical description of the problem.
Many real-world networks exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Particularly in social networks, the contribution to the total assortativity varies with degree, featuring a distinctive peak slightly past the average degree. The way traditional models imprint assortativity on top of pre-defined topologies is via degree-preserving link permutations, which however destroy the particular graph's hierarchical traits of clustering. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties and tunable realistic assortativity. In our approach, two distinct populations of nodes are added to an initial network seed: one (the followers) that abides by usual preferential rules, and one (the potential leaders) connecting via anti-preferential attachments, i.e. selecting lower degree nodes for their initial links. The latter nodes come to develop a higher average degree, and convert eventually into the final hubs. Examining the evolution of links in Facebook, we present empirical validation for the connection between the initial anti-preferential attachment and long term high degree. Thus, our work sheds new light on the structure and evolution of social networks.
Social media outlets such as Twitter constitute valuable data sources for understanding human activities in the virtual world from a geographic perspective. This paper examines spatial distribution of tweets and densities within cities. The cities refer to natural cities that are automatically aggregated from a country's small street blocks, so called city blocks. We adopted street blocks (rather than census tracts) as the basic geographic units and topological center (rather than geometric center) in order to assess how tweets and densities vary from the center to the peripheral border. We found that, within a city from the center to the periphery, the tweets first increase and then decrease, while the densities decrease in general. These increases and decreases fluctuate dramatically, and differ significantly from those if census tracts are used as the basic geographic units. We also found that the decrease of densities from the center to the periphery is less significant, and even disappears, if an arbitrarily defined city border is adopted. These findings prove that natural cities and their topological centers are better than their counterparts (conventionally defined cities and city centers) for geographic research. Based on this study, we believe that tweet densities can be a good surrogate of population densities. If this belief is proved to be true, social media data could help solve the dispute surrounding exponential or power function of urban population density.
Keywords: Big data, natural cities, street blocks, urban density, topological distance
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 93, 032208] Published Mon Mar 07, 2016
Author(s): Klementyna Szwaykowska, Ira B. Schwartz, Luis Mier-y-Teran Romero, Christoffer R. Heckman, Dan Mox, and M. Ani Hsieh
The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is a subject of great interest in a wide range of application areas, ranging from engineering and physics to biology. In this paper, we model and experimentally realize a mixed-reality large-scale swarm of d…
[Phys. Rev. E 93, 032307] Published Mon Mar 07, 2016
Stability assessment methods for dynamical systems have recently been complemented by basin stability and derived measures, i.e. probabilistic statements whether systems remain in a basin of attraction given a distribution of perturbations. This requires numerical estimation via Monte-Carlo sampling and integration of differential equations. Here, we analyze the applicability of basin stability to systems with basin geometries challenging for this numerical method, having fractal basin boundaries and riddled or intermingled basins of attraction. We find that numerical basin stability estimation is still meaningful for fractal boundaries but reaches its limits for riddled basins with holes.
We give upper and lower bounds on the information-theoretic threshold for community detection in the stochastic block model. Specifically, let $k$ be the number of groups, $d$ be the average degree, the probability of edges between vertices within and between groups be $c_\mathrm{in}/n$ and $c_\mathrm{out}/n$ respectively, and let $\lambda = (c_\mathrm{in}-c_\mathrm{out})/(kd)$. We show that, when $k$ is large, and $\lambda = O(1/k)$, the critical value of $d$ at which community detection becomes possible -- in physical terms, the condensation threshold -- is \[ d_c = \Theta\!\left( \frac{\log k}{k \lambda^2} \right) \, , \] with tighter results in certain regimes. Above this threshold, we show that the only partitions of the nodes into $k$ groups are correlated with the ground truth, giving an exponential-time algorithm that performs better than chance -- in particular, detection is possible for $k \ge 5$ in the disassortative case $\lambda < 0$ and for $k \ge 11$ in the assortative case $\lambda > 0$. (Similar upper bounds were obtained independently by Abbe and Sandon.) Below this threshold, we use recent results of Neeman and Netrapalli (who generalized arguments of Mossel, Neeman, and Sly) to show that no algorithm can label the vertices better than chance, or even distinguish the block model from an Erd\H{o}s-R\'enyi random graph with high probability. We also rely on bounds on certain functions of doubly stochastic matrices due to Achlioptas and Naor; indeed, our lower bound on $d_c$ is the second moment lower bound on the $k$-colorability threshold for random graphs with a certain effective degree.
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 93, 032203] Published Fri Mar 04, 2016
Populations of oscillators can display a variety of synchronization patterns depending on the oscillators' intrinsic coupling and the coupling between them. We consider two coupled, symmetric (sub)populations with unimodal frequency distributions and show that the resulting synchronization patterns may resemble those of a single population with bimodally distributed frequencies. Our proof of the equivalence of their stability, dynamics, and bifurcations, is based on an Ott-Antonsen ansatz. The generalization to networks consisting of multiple (sub)populations vis-\`a-vis networks with multimodal frequency distributions, however, appears impossible.
We present a universal characterization scheme for chimera states applicable to both numerical and experimental data sets. The scheme is based on two correlation measures that enable a meaningful definition of chimera states as well as their classification into three categories: stationary, turbulent and breathing. In addition, these categories can be further subdivided according to the time-stationarity of these two measures. We demonstrate that this approach both is consistent with previously recognized chimera states and enables us to classify states as chimeras which have not been categorized as such before. Furthermore, the scheme allows for a qualitative and quantitative comparison of experimental chimeras with chimeras obtained through numerical simulations.
Vaccination and outbreak monitoring are essential tools for preventing and minimizing outbreaks of infectious diseases. Targeted strategies, where the individuals most important for monitoring or preventing outbreaks are selected for intervention, offer a possibility to significantly improve these measures. Although targeted strategies carry a strong potential, identifying optimal target groups remains a challenge. Here we consider the problem of identifying target groups based on digital communication networks (telecommunication, online social media) in order to predict and contain an infectious disease spreading on a real-world person-to-person network of more than 500 individuals. We show that target groups for efficient outbreak monitoring can be determined based on both telecommunication and online social network information. In case of vaccination the information regarding the digital communication networks improves the efficacy for short-range disease transmissions but, surprisingly, performance is severely reduced in the case of long-range transmission. These results are robust with respect to the strategy used to identify targeted individuals and time-gap between identification of targets and the intervention. Thus, we demonstrate that data available from telecommunication and online social networks can greatly improve epidemic control measures, but it is important to consider the details of the pathogen spreading mechanism when such policies are applied.
We present a universal characterization scheme for chimera states applicable to both numerical and experimental data sets. The scheme is based on two correlation measures that enable a meaningful definition of chimera states as well as their classification into three categories: stationary, turbulent and breathing. In addition, these categories can be further subdivided according to the time-stationarity of these two measures. We demonstrate that this approach both is consistent with previously recognized chimera states and enables us to classify states as chimeras which have not been categorized as such before. Furthermore, the scheme allows for a qualitative and quantitative comparison of experimental chimeras with chimeras obtained through numerical simulations.
The control of network-coupled nonlinear dynamical systems is an active area of research in the nonlinear science community. Coupled oscillator networks represent a particularly important family of nonlinear systems, with applications ranging from the power grid to cardiac excitation. Here we study the control of network-coupled limit cycle oscillators, extending previous work that focused on phase oscillators. Based on stabilizing a target fixed point, our method aims to attain complete frequency synchronization, i.e., consensus, by applying control to as few oscillators as possible. We develop two types of control. The first type directs oscillators towards to larger amplitudes, while the second does not. We present numerical examples of both control types and comment on the potential failures of the method.
We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.
Six Susceptible-Infected-Susceptible Models on Scale-free Networks
Scientific Reports, Published online: 3 March 2016; doi:10.1038/srep22506
The control of network-coupled nonlinear dynamical systems is an active area of research in the nonlinear science community. Coupled oscillator networks represent a particularly important family of nonlinear systems, with applications ranging from the power grid to cardiac excitation. Here we study the control of network-coupled limit cycle oscillators, extending previous work that focused on phase oscillators. Based on stabilizing a target fixed point, our method aims to attain complete frequency synchronization, i.e., consensus, by applying control to as few oscillators as possible. We develop two types of control. The first type directs oscillators towards to larger amplitudes, while the second does not. We present numerical examples of both control types and comment on the potential failures of the method.
In dynamical systems with divided phase space, the vicinity of the boundary between regular and chaotic regions is often "sticky," that is, trapping orbits from the chaotic region for long times. Here, we investigate the stickiness in the simplest mushroom billiard, which has a smooth such boundary, but surprisingly subtle behaviour. As a measure of stickiness, we investigate P(t), the probability of remaining in the mushroom cap for at least time t given uniform initial conditions in the chaotic part of the cap. The stickiness is sensitively dependent on the radius of the stem r via the Diophantine properties of rho = (2/\pi) arccos r. Almost all rho give rise to families of marginally unstable periodic orbits (MUPOs) where P(t) ~ C/t, dominating the stickiness of the boundary. Here we consider the case where rho is MUPO-free and has continued fraction expansion with bounded partial quotients. We show that t^2 P(t) is bounded, varying infinitely often between values whose ratio is at least 32/27. When rho has an eventually periodic continued fraction expansion, that is, a quadratic irrational, t^2 P(t) converges to a log-periodic function. In general, we expect less regular behaviour, with upper and lower exponents lying between 1 and 2. The results may shed light on the parameter dependence of boundary stickiness in annular billiards and generic area preserving maps.