arXiv subjects

Michael Small

Publications and source records attributed to Michael Small.

At least 73 records · Page 4Linked to original sources

arXiv2010

Recurrence-based time series analysis by means of complex network methods

Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related with the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the individual methods are discussed and illustrated for paradigmatic examples of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series analysis and, hence, substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) approaches.

nlin.CD↗

arXiv2010

Rich-club connectivity dominates assortativity and transitivity of complex networks

Rich-club, assortativity and clustering coefficients are frequently-used measures to estimate topological properties of complex networks. Here we find that the connectivity among a very small portion of the richest nodes can dominate the assortativity and clustering coefficients of a large network, which reveals that the rich-club connectivity is leveraged throughout the network. Our study suggests that more attention should be payed to the organization pattern of rich nodes, for the structure of a complex system as a whole is determined by the associations between the most influential individuals. Moreover, by manipulating the connectivity pattern in a very small rich-club, it is sufficient to produce a network with desired assortativity or transitivity. Conversely, our findings offer a simple explanation for the observed assortativity and transitivity in many real world networks --- such biases can be explained by the connectivities among the richest nodes.

physics.soc-ph↗

arXiv2009

Revising the simple measures of assortativity in complex networks

We find that traditional statistics for measuring degree mixing are strongly affected by superrich nodes. To counteract and measure the effect of superrich nodes, we propose a paradigm to quantify the mixing pattern of a real network in which different mixing patterns may appear among low-degree nodes and among high-degree nodes. The new paradigm and the simple revised measure uncover the true complex degree mixing patterns of complex networks with superrich nodes. The new method indicates that some networks show a false disassortative mixing induced by superrich nodes, and have no tendency to be genuinely disassortative. Our results also show that the previously observed fragility of scale-free networks is actually greatly exacerbated by the presence of even a very small number of superrich nodes.

physics.soc-ph↗

arXiv2009

Mapping from Architecture to Dynamics: A Unified View of Dynamical Processes on Networks

Although it is unambiguously agreed that structure plays a fundamental role in shaping the dynamics of complex systems, this intricate relationship still remains unclear. We investigate a general computational transformation by which we can map the network topology directly to the dynamical patterns emergent on it -- independent of the nature of the dynamical process. We find that many seemingly diverse dynamical processes such as coupled oscillators and diffusion phenomena can all be understood and unified through this same procedure. Using the multiscale complexity measure derived form the structure-dynamics transformation, we find that the topological features like hierarchy, heterogeneity and modularity all result in higher complexity. This result suggests a universal principle: it is the desire for functional diversity that drives the evolution of network architecture.

cond-mat.dis-nn↗

arXiv2009

Flocking of Multi-agent Dynamical Systems Based on Pseudo-leader Mechanism

Flocking behavior of multiple agents can be widely observed in nature such as schooling fish and flocking birds. Recent literature has proposed the possibility that flocking is possible even only a small fraction of agents are informed of the desired position and velocity. However, it is still a challenging problem to determine which agents should be informed or have the ability to detect the desired information. This paper aims to address this problem. By combining the ideas of virtual force and pseudo-leader mechanism, where a pseudo-leader represents an agent who can detect the desired information, we propose a scheme for choosing pseudo-leaders in a multi-agent group. The presented scheme can be applied to a multi-agent group even with an unconnected or switching neighbor graph. Experiments are given to show that the methods presented in this paper are of high accuracy and perform well.

nlin.CD↗

arXiv2007

Asymptotically pivotal statistic for surrogate testing with extended hypothesis

The method of surrogate data provides a framework for testing observed data against a hierarchy of alternative hypotheses. The aim of applying this method is to exclude the possibility that the data are consistent with simple linear explanations before seeking complex nonlinear causes. However, in recent time the method has attracted considerable criticism, largely as a result of ambiguity about the formation of the underlying null hypotheses, or about the power of the chosen statistic. In this communication we show that by employing a special family of ranks statistics these problems can be avoided and the method of surrogate data placed of a firm statistical foundation.

nlin.CD↗

arXiv2006

Exact nonparametric inference for detection of nonlinear determinism

We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the advantages of our scheme is that, it can provide exact confidence levels for our null hypothesis tests. Furthermore, the exactness is applicable for finite samples with arbitrary length. We demonstrate the test power of this statistic through several examples.

nlin.CD↗

arXiv2006

Surrogate data method applied to nonlinear time series

The surrogate data method is widely applied as a data dependent technique to test observed time series against a barrage of hypotheses. However, often the hypotheses one is able to address are not those of greatest interest, particularly for system known to be nonlinear. In the review we focus on techniques which overcome this shortcoming. We summarize a number of recently developed surrogate data methods. While our review of surrogate methods is not exhaustive, we do focus on methods which may be applied to experimental, and potentially nonlinear, data. In each case, the hypothesis being tested is one of the interests to the experimental scientist.

nlin.CD↗

arXiv2006

Analysis of telephone network traffic based on a complex user network

The traffic in telephone networks is analyzed in this paper. Unlike the classical traffic analysis where call blockings are due to the limited channel capacity, we consider here a more realistic cause for call blockings which is due to the way in which users are networked in a real-life human society. Furthermore, two kinds of user network, namely, the fully-connected user network and the scale-free network, are employed to model the way in which telephone users are connected. We show that the blocking probability is generally higher in the case of the scale-free user network, and that the carried traffic intensity is practically limited not only by the network capacity but also by the property of the user network.

physics.soc-ph↗

arXiv2005

Optimal phase space projection for noise reduction

In this communication we will re-examine the widely studied technique of phase space projection. By imposing a time domain constraint (TDC) on the residual noise, we deduce a more general version of the optimal projector, which includes those appearing in previous literature as subcases but does not assume the independence between the clean signal and the noise. As an application, we will apply this technique for noise reduction. Numerical results show that our algorithm has succeeded in augmenting the signal-to-noise ratio (SNR) for simulated data from the Rössler system and experimental speech record.

nlin.CD↗

arXiv2005

Surrogate Test to Distinguish between Chaotic and Pseudoperiodic Time Series

In this communication a new algorithm is proposed to produce surrogates for pseudoperiodic time series. By imposing a few constraints on the noise components of pseudoperiodic data sets, we devise an effective method to generate surrogates. Unlike other algorithms, this method properly copes with pseudoperiodic orbits contaminated with linear colored observational noise. We will demonstrate the ability of this algorithm to distinguish chaotic orbits from pseudoperiodic orbits through simulation data sets from theRössler system. As an example of application of this algorithm, we will also employ it to investigate a human electrocardiogram (ECG) record.

nlin.CD↗

arXiv2004

Axino Dark Matter and the CMSSM

If the axino is the lightest superpartner and satisfies cosmological bounds, including a preferred range of the relic abundance of cold dark matter, then the usual stringent constraints on the parameter space of the CMSSM become greatly relaxed. The lightest superpartner of the usual CMSSM spectrum will appear to be stable in collider experiments but will not necessarily obey relic abundance constraints. It may be either neutral (lightest neutralino) or charged (typically a stau). With the axino as cold dark matter, large regions of the CMSSM, often corresponding to heavy superpartners, become allowed, depending on the axino mass and the reheating temperature.

hep-ph↗

arXiv2003

Plausible models for propagation of the SARS virus

Using daily infection data for Hong Kong we explore the validity of a variety of models of disease propagation when applied to the SARS epidemic. Surrogate data methods show that simple random models are insufficient and that the standard epidemic susceptible-infected-removed model does not fully account for the underlying variability in the observed data. As an alternative, we consider a more complex small world network model and show that such a structure can be applied to reliably produce simulations quantitative similar to the true data. The small world network model not only captures the apparently random fluctuation in the reported data, but can also reproduces mini-outbreaks such as those caused by so-called ``super-spreaders'' and in the Hong Kong housing estate Amoy Gardens.

q-bio.PE↗

arXiv2003

Modelling of SARS for Hong Kong

A simplified susceptible-infected-recovered (SIR) epidemic model and a small-world model are applied to analyse the spread and control of Severe Acute Respiratory Syndrome (SARS) for Hong Kong in early 2003. From data available in mid April 2003, we predict that SARS would be controlled by June and nearly 1700 persons would be infected based on the SIR model. This is consistent with the known data. A simple way to evaluate the development and efficacy of control is described and shown to provide a useful measure for the future evolution of an epidemic. This may contribute to improve strategic response from the government. The evaluation process here is universal and therefore applicable to many similar homogeneous epidemic diseases within a fixed population. A novel model consisting of map systems involving the Small-World network principle is also described. We find that this model reproduces qualitative features of the random disease propagation observed in the true data. Unlike traditional deterministic models, scale-free phenomena are observed in the epidemic network. The numerical simulations provide theoretical support for current strategies and achieve more efficient control of some epidemic diseases, including SARS.

q-bio.PE↗

arXiv2003

Geometric measures of redundance and irrelevance tradeoff exponent to choose suitable delay times for continuous systems

Using the concept of the geometric measures of redundance and irrelevance tradeoff exponent (RITE)}, we present a new method to determine suitable delay times for continuous systems. After applying the RITE algorithm to both simulation and experimental observations, we find the results obtained are close to those obtained from the criterion of the average mutual information (AMI), while the RITE algorithm has the following advantages: simple implementation, reasonable computational cost and robust performance against observational noise.

nlin.CD↗

arXiv2003

Optimal time delay embedding for nonlinear time series modeling

When building linear or nonlinear models one is faced with the problem of selecting the best set of variable with which to predict the future dynamics. In nonlinear time series analysis the problem is to select the correct time delays in the time delay embedding. We propose a new technique which can quantify the suitability of a particular set of variables and we suggests a computationally efficient scheme to determine the best non-uniform time delay embedding for modeling of time series. Our results are based on the assumption that, in general, the variables which give the best local constant model will also give the best nonlinear model. In a wide variety of experimental and simulated systems we find that this method produces dynamics that are more realistic and predictions that are more accurate than standard uniform embeddings.

nlin.CD↗

arXiv2003

Estimating the distribution of dynamic invariants: Illustrated with an application to human photo-plethysmographic time series

Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence intervals, however such confidence intervals do not reflect variability in the underlying dynamics. In this communication we propose a surrogate based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing physiological states and provide conclusive evidence that correlation dimension is capable of differentiating between three (but not all four) of these states.

nlin.CD↗

arXiv2003

Optimal embedding parameters: A modelling paradigm

Reconstruction of a dynamical system from a time series requires the selection of two parameters, the embedding dimension $d_e$ and the embedding lag $τ$. Many competing criteria to select these parameters exist, and all are heuristic. Within the context of modeling the evolution operator of the underlying dynamical system, we show that one only need be concerned with the product $d_eτ$. We introduce an information theoretic criteria for the optimal selection of the embedding window $d_w=d_eτ$. For infinitely long time series this method is equivalent to selecting the embedding lag that minimises the nonlinear model prediction error. For short and noisy time series we find that the results of this new algorithm are data dependent and superior to estimation of embedding parameters with the standard techniques.

physics.data-an↗