The analysis of an epidemic model on networks

The paper consider an epidemic model with birth and death on networks. We derive the epidemic threshold R₀ dependent on birth rate b, death rate d (natural death) and μ from the infectious disease and natural death, and cure rate γ. And the stability of the equilibriums (the disease-free equilibrium and endemic equilibrium) are analysed. Finally, the effects of various immunization schemes are studied and compared. We show that both targeted, and acquaintance immunization strategies compare favorably to a proportional scheme in terms of effectiveness. For active immunization, the threshold is easier to apply practically. To illustrate our theoretical analysis, some numerical simulations are also included.     

Global analysis of an SIS model with an infective vector on complex networks

In this paper, a modified SIS model with an infective vector on complex networks is proposed and analyzed, which incorporates some infectious diseases that are not only transmitted by a vector, but also spread by direct contacts between human beings. We treat direct human contacts as a social network and assume spatially homogeneous mixing between vector and human populations. By mathematical analysis, we obtain the basic reproduction number R₀ and study the effects of various immunization schemes. For the network model, we prove that if R₀<1, the disease-free equilibrium is globally asymptotically stable, otherwise there exists an unique endemic equilibrium such that it is globally attractive. Our theoretical results are confirmed by numerical simulations and suggest a promising way for the control of infectious diseases.     

Stability analysis of data-driven local model networks

This article discusses stability analysis of data-driven dynamic local model networks. In contrast to traditional fuzzy modelling, the structure and complexity of such model architectures is not unique when only observed input- and output data are available for their parametrization. The present article complements the well-known trade-off between accuracy and complexity by the notion of stability. For this purpose, existing Lyapunov stability criteria for local model networks are extended by a decay rate which represents a scalar and quantitative stability measure. It allows to compare models with different degrees of complexity also in view of their stability. For some of the commonly available Lyapunov stability criteria, the individual local model transitions are crucial. Therefore, in this article, an approach is introduced to determine the actually occurring model transitions by means of the identification data. The methods presented in the article are illustrated and discussed by means of a simulation example. It is shown how model complexity and the related approximation quality can have an adverse impact on the stability and how the outcome of different Lyapunov criteria is affected by the proper determination of local model transitions.     

Performance analysis of multi-frame message spreading in delay tolerant networks

Communication opportunities in delay tolerant networks are uncertain, so the message is transmitted in a store-carry-forward way, which depends on the contact between nodes. To use the contact efficiently, the message is often divided into many bundles, which are very small and can be transmitted successfully in one contact. Such multi-frame spreading algorithm is very important, but state of the art works just assume that the message is very small and has only one bundle. This paper proposes a theoretical framework based on mean field limit to evaluate the epidemic-like multi-frame spreading algorithm for the first time. In addition, the selfish behaviors can have certain impact on the store-carry-forward communication mode, so we extend our model to the case that nodes are selfish. Simulations show the accuracy of our model. Numerical results show that the more bundles the message has, the lower the average delivery ratio will be. In addition, the selfish behaviors can make the performance be worse.     

A preferential attachment model with Poisson growth for scale-free networks

We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.     

Threshold behaviour of a stochastic SIR model

In this paper, we investigate the threshold behaviour of a susceptible-infected-recovered (SIR) epidemic model with stochastic perturbation. When the noise is small, we show that the threshold determines the extinction and persistence of the epidemic. Compared with the corresponding deterministic system, this value is affected by white noise, which is less than the basic reproduction number of the deterministic system. On the other hand, we obtain that the large noise will also suppress the epidemic to prevail, which never happens in the deterministic system. These results are illustrated by computer simulations.     

Viral conductance: Quantifying the robustness of networks with respect to spread of epidemics

In this paper, we propose a novel measure, viral conductance (VC), to assess the robustness of complex networks with respect to the spread of SIS epidemics. In contrast to classical measures that assess the robustness of networks based on the epidemic threshold above which an epidemic takes place, the new measure incorporates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, we show that VC provides more insight about the robustness of networks than does the epidemic threshold. We also address the paradoxical robustness of Barabási–Albert preferential attachment networks. Even though this class of networks is characterized by a vanishing epidemic threshold, the epidemic requires high effective infection strength to cause a major outbreak. On the contrary, in homogeneous networks the effective infection strength does not need to be very much beyond the epidemic threshold to cause a major outbreak. To overcome computational complexities, we propose a heuristic to compute the VC for large networks with high accuracy. Simulations show that the heuristic gives an accurate approximation of the exact value of the VC. Moreover, we derive upper and lower bounds of the new measure. We also apply the new measure to assess the robustness of different types of network structures, i.e. Watts–Strogatz small world, Barabási–Albert, correlated preferential attachment, Internet AS-level, and social networks. The extensive simulations show that in Watts–Strogatz small world networks, the increase in probability of rewiring decreases the robustness of networks. Additionally, VC confirms that the irregularity in node degrees decreases the robustness of the network. Furthermore, the new measure reveals insights about design and mitigation strategies of infrastructure and social networks.     

Global asymptotic stability and a property of the SIS model on bipartite networks

In this paper, we study an SIS model on bipartite networks, in which the network structure and a connectivity-dependent infection scheme are considered. Applying the theory of the multigroup model, we prove the existence and the asymptotic stability of the endemic equilibrium. And then we examine the ratio between the densities of infected female and male individuals on the bipartite networks. In particular, we find that when the scale exponent (γ_F) of females is equal to and that of males (γ_M), the ratio is only determined by the scale exponents and the proportion between the infection rates of females and males (λ_F/λ_M). We also present a result for the ratio by numerical simulations when γ_F≠γ_M.     

Local estimates for eigenvector-like centralities of complex networks

We will analyze several centrality measures by giving a general framework that includes the Bonacich centrality, PageRank centrality or in-degree vector, among others. We will get some local scale estimators for such global measures by giving some geometrical characterizations and some deviation results that help to quantify the error of approximating a spectral centrality by a local estimator.     

A note on the Estrada-Hatano communicability algorithm for detecting community structure in complex networks

Recently Estrada and Hatano proposed an algorithm for the detection of community structure in complex networks using the concept of network communicability. Here we amend this algorithm by eliminating the subjectivity of choosing degree of overlapping and including an additional check of the fitness of detected communities. We show that this amendment can detect some communities which remain undetected by Estrada and Hatano’s algorithm. For instance, let G(p, q) be a graph obtained from two cliques, K_p and K_q(p ? q ? 3), joined by a single edge. It is apparent that this graph contains two communities, namely the two cliques. However, Estrada and Hatano’s algorithm detects only K_q as a community when p is sufficiently larger than q. Our algorithm correctly detects both communities. Also, our method also finds the correct community structure in one of the classic benchmark networks, the Zachary karate club.     

Robust global synchronization of complex networks with neutral-type delayed nodes

In this paper, the problems of robust global exponential synchronization for a class of complex networks with time-varying delayed couplings are considered. Each node in the network is composed of a class of delayed neural networks described by a nonlinear delay differential equation of neutral-type. Since model errors commonly exist in practical applications, the parameter uncertainties are involved in the considered model. Sufficient conditions that ensure the complex networks to be robustly globally exponentially synchronized are obtained by using the Lyapunov functional method and some properties of Kronecker product. An illustrative example is presented to show the effectiveness of the proposed approach.     

Probing and shaping the information transfer of noise-perturbed complex networks via Markov Chain analysis

We demonstrate a recursive computational procedure based on the distributions of first passage time on Markov Chains that can mathematically characterize noise-driven processes in complex networks. Considering examples of both real (Enron email) and artificial (Ravasz-Barabasi) networks perturbed by noise using Monte Carlo simulations, our method accurately recovers the percentages that information will be transferred to the intended receivers. The paradigm reported here captures and provides explanation to the recent results of Czaplicka et al. (Nature Sci. Rep. 2013) showing that the presence of noise can actually enhance the transfer of information in a hierarchical complex network. Finally, we illustrate how adaptive thresholding guided by our developed procedure can be used to engineer or shape the dynamic range of networks operating in a noisy environment.     

Stability analysis for an epidemic model with stage structure

An epidemic model with stage structure is formulated. The period of infection is partitioned into the early and later stages according to the developing process of infection, and the infectious individuals in the different stages have the different ability of transmitting disease. The constant recruitment rate and exponential natural death, as well as the disease-related death, are incorporated into the model. The basic reproduction number of this model is determined by the method of next generation matrix. The global stability of the disease-free equilibrium and the local stability of the endemic equilibrium are obtained; the global stability of the endemic equilibrium is got under the case that the infection is not fatal.     

Synchronization stability of general complex dynamical networks with time-varying delays: A piecewise analysis method

The synchronization problem of some general complex dynamical networks with time-varying delays is investigated. Both time-varying delays in the network couplings and time-varying delays in the dynamical nodes are considered. The delays considered in this paper are assumed to vary in an interval, where the lower and upper bounds are known. Based on a piecewise analysis method, the variation interval of the time delay is firstly divided into several subintervals, by checking the variation of the derivative of a Lyapunov function in every subinterval, then the convexity of matrix function method and the free weighting matrix method are fully used in this paper. Some new delay-dependent synchronization stability criteria are derived in the form of linear matrix inequalities. Two numerical examples show that our method can lead to much less conservative results than those in the existing references.     

Fuzzy regression analysis using neural networks

In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can be applied to more complex systems than the existing LP based methods. First we propose learning algorithms of neural networks for determining a nonlinear interval model from the given input-output patterns. A nonlinear interval model whose outputs approximately include all the given patterns can be determined by two neural networks. Next we show two methods for deriving nonlinear fuzzy models from the interval model determined by the proposed algorithms. Nonlinear fuzzy models whose h-level sets approximately include all the given patterns can be derived. Last we show an application of the proposed methods to a real problem.