Social network topology: a Bayesian approach

Elucidating the pattern of links within social networks is a challenging problem. Of particular difficulty is determining the existence of links in those groups that take active measures to conceal their internal connections, such as terrorist or criminal organizations where conventional social network analysis data-gathering techniques cannot be applied. Network representations of such organizations are useful, because they often represent a useful point of departure in thinking both about the potential capabilities of organizations and how to conduct effective measures to counter them. Developing an effective process for constructing such network representations from incomplete and limited data of variable quality is a topic of much current interest. Here, a method based on Bayesian inference is presented that probabilistically infers the existence of links within a social network. It is tested on data from open source publications. Additionally, the method represents a possible approach to dynamically modelling networks, as it is feasible to calculate how a network will reconfigure following an intervention.     

Least-cost network topology design for a new service

We describe an implementation of the tabu search metaheuristic that effectively finds a low-cost topology for a communications network to provide a centralized new service. Our results are compared to those of a greedy algorithm which applies corresponding decision rules, but without the guidance of the tabu search framework. These problems are difficult computationally, representing integer programs that can involve as many as 10,000 integer variables and 2000 constraints in practical applications. The tabu search results approach succeeded in obtaining significant improvements over the greedy approach, yielding optimal solutions to problems small enough to allow independent verification of optimality status and, more generally, yielding both absolute and percentage cost improvements that did not deteriorate with increasing problem size.This research was partially supported by the Air Force Office of Scientific Research and the Office of Naval Research Contract No. F49629-90-C-0033.     

Stability and bifurcation of a two-neuron network with distributed time delays

In this paper we study the stability and bifurcation of the trivial solution of a two-neuron network model with distributed time delays. This model consists of two identical neurons, each possessing nonlinear instantaneous self-feedback and connected to the other neuron with continuously distributed time delays. We first examine the local asymptotic stability of the trivial solution by studying the roots of the corresponding characteristic equation, and then describe the stability and instability regions in the parameter space consisting of the self-feedback strength and the product of the connection strengths between the neurons. It is further shown that the trivial solution may lose its stability via a certain type of bifurcation such as a Hopf bifurcation or a pitchfork bifurcation. In addition, the criticality of Hopf bifurcation is investigated by means of the normal form theory. We also provide numerical evidence to support our theoretical analyses.     

Local dynamics of a spatially distributed logistic equation with delay and large transport coefficient

We study the behavior of all solutions in some sufficiently small neighborhood of the positive equilibrium of the spatially distributed Hutchinson equation with diffusion and advection. On the basis of the method of invariant integral manifolds and the method of normal forms, we consider the dynamics for the case critical in the problem on the stability of the stationary solution. We show that, for a sufficiently large value of the transport (advection) coefficient, the critical case has infinite dimension. We construct a quasinormal form, which is a nonlinear parabolic boundary value problem with a deviation in the space variable and which plays the role of a normal form; i.e., its nonlocal dynamics defines the local dynamics of the original equation. Secondary bifurcations in the quasinormal form are considered for the case close to the critical case in the problem on the stability of the stationary solution.     

Stability and bifurcation analysis for a neural network model with discrete and distributed delays

In this paper, a two‐neuron network with both discrete and distributed delays is considered. With the corresponding characteristic equation analyzed, the local stability of the trivial equilibrium is investigated. With the discrete time delay taken as a bifurcation parameter, the existence of Hopf bifurcation is established. Moreover, formulae for determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are derived. Finally, numerical simulations are carried out to illustrate the main results and further to exhibit that there is a characteristic sequence of bifurcations leading to a chaotic dynamics, which implies that the system admits rich and complex dynamics. Copyright © 2012 John Wiley & Sons, Ltd.     

Stability and Hopf bifurcation analysis on a four-neuron BAM neural network with distributed delays

In this paper, a four-neuron BAM neural network with distributed delays is considered, where kernels are chosen as weak kernels. Its dynamics is studied in terms of local stability analysis and Hopf bifurcation analysis. By choosing the average delay as a bifurcation parameter and analyzing the associated characteristic equation, Hopf bifurcation occurs when the bifurcation parameter passes through some exceptive values. The stability of bifurcating periodic solutions and a formula for determining the direction of Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. Finally, numerical simulation results are given to validate the theorem obtained.     

Robust stability analysis of static neural network with S-type distributed delays

In this paper, we investigate local robust stability of static neural network (SNN) with S-type distributed delays. We derive some new sufficient conditions for local robust stability of equilibrium points and estimate attracting domains of equilibrium points except isolated equilibrium points. Our results not only show local robust stability of equilibrium points but also allow much broader application for static neural network with or without delays. It is shown that our results are new and improve corresponding results existing in the previous literature.     

D-optimal design of a monitoring network for parameter estimation of distributed systems

This paper addresses the design of a network of observation locations in a spatial domain that will be used to estimate unknown parameters of a distributed parameter system. We consider a setting where we are given a finite number of possible sites at which to locate a sensor, but cost constraints allow only some proper subset of them to be selected. We formulate this problem as the selection of the gauged sites so as to maximize the log-determinant of the Fisher information matrix associated with the estimated parameters. The search for the optimal solution is performed using the branch-and-bound method in which an extremely simple and efficient technique is employed to produce an upper bound to the maximum objective function. Its idea consists in solving a relaxed problem through the application of a simplicial decomposition algorithm in which the restricted master problem is solved using a multiplicative algorithm for optimal design. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a two-dimensional convective diffusion process.     

Hopf bifurcation in Cohen–Grossberg neural network with distributed delays

In this paper, we discuss the stability and bifurcation of the distributed delays Cohen–Grossberg neural networks with two neurons. By choosing the average delay as a bifurcation parameter, we prove that Hopf bifurcation occurs. The stability of bifurcating periodic solutions and the direction of Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. Finally, numerical simulation results are given to support the theoretical predictions.     

Broad-scale small-world network topology induces optimal synchronization of flexible oscillators

The discovery of small-world and scale-free properties of many man-made and natural complex networks has attracted increasing attention. Of particular interest is how the structural properties of a network facilitate and constrain its dynamical behavior. In this paper we study the synchronization of weakly coupled limit-cycle oscillators in dependence on the network topology as well as the dynamical features of individual oscillators. We show that flexible oscillators, characterized by near zero values of divergence, express maximal correlation in broad-scale small-world networks, whereas the non-flexible (rigid) oscillators are best correlated in more heterogeneous scale-free networks. We found that the synchronization behavior is governed by the interplay between the networks global efficiency and the mutual frequency adaptation. The latter differs for flexible and rigid oscillators. The results are discussed in terms of evolutionary advantages of broad-scale small-world networks in biological systems.     

Fixed-time synchronization of a reaction-diffusion BAM neural network with distributed delay and its application to image encryption

In this paper, the fixed-time synchronization of reaction-diffusion BAM neural networks is investigated, where both discrete and distributed delays are taken into account. Combining Lyapunov stability theory and several integral inequalities, fixed-time synchronization criteria are established. Through sensitivity analysis, we find the key controller parameters that have a great influence on the maximum settling time. Using the chaotic sequences generated by the neural network, the color image can be encrypted by the Arnold Cat Map and the pixel diffusion. Experiments show that the image encryption algorithm designed in this paper has good properties of security and anti-attacking, which meets the requirements for the secure transmission of image information.     

A mathematical model for a distributed attack on targeted resources in a computer network

A mathematical model has been developed to analyze the spread of a distributed attack on critical targeted resources in a network. The model provides an epidemic framework with two sub-frameworks to consider the difference between the overall behavior of the attacking hosts and the targeted resources. The analysis focuses on obtaining threshold conditions that determine the success or failure of such attacks. Considering the criticality of the systems involved and the strength of the defence mechanism involved, a measure has been suggested that highlights the level of success that has been achieved by the attacker. To understand the overall dynamics of the system in the long run, its equilibrium points have been obtained and their stability has been analyzed, and conditions for their stability have been outlined.     

A shunting inhibitory cellular neural network with continuously distributed delays of neutral type

In this paper, a shunting inhibitory cellular neural network with continuously distributed delays of neutral type is considered. We establish some new results about the existence and exponential stability of the almost periodic solution for the shunting inhibitory cellular neural network.     

On the topology of foliations with a first integral

The main objective of this article is to study the topology of the fibers of a generic rational function of the type in the projective space of dimension two. We will prove that the action of the monodromy group on a single Lefschetz vanishing cycle generates the first homology group of a generic fiber of . In particular, we will prove that for any two Lefschetz vanishing cycles ₀ and ₁ in a regular compact fiber of , there exists a mondromyh such thath( ₀)=± ₁.Partially supported by CNPq-Brazil.     

Compact images of spaces with a weaker metric topology

If X is a space that can be mapped onto a metric space by a one-to-one mapping, then X is said to have a weaker metric topology. In this paper, we give characterizations of sequence-covering compact images and sequentially-quotient compact images of spaces with a weaker metric topology. The main results are that (1) Y is a sequence-covering compact image of a space with a weaker metric topology if and only if Y has a sequence of point-finite cs-covers such that for each y ∈ Y. (2) Y is a sequentially-quotient compact image of a space with a weaker metric topology if and only if Y has a sequence of point-finite cs*-covers such that for each y ∈ Y. Supported by the NNSF(10471084) of China.     

Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts–Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance.