Search

Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network's underlying connection pattern given a single and noisy instantiation. While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications. Consequently, network modeling based on these methods either suffers from model misspecification or relies on additional model selection procedures that are not well understood in theory and can potentially be unstable in practice. To address this difficulty, we propose a mixing strategy that leverages available arbitrary models to improve their individual performances. The proposed method is computationally efficient and almost tuning-free for network modeling. We show that the proposed method performs equally well as the oracle estimate when the true model is included as individual candidates. More importantly, the method remains robust and outperforms all current estimates even when the models are misspecified. Extensive simulation examples are used to verify the advantage of the proposed mixing method. Evaluation of link prediction performance on more than 500 real-world networks from different domains also demonstrates the universal competitiveness of the mixing method across multiple domains. for this article are available online. [ABSTRACT FROM AUTHOR]

The performance of the cognitive non-orthogonal multiple access (NOMA) systems with random locations is examined in the present paper. More precisely, the positions of the secondary receivers (SRs) are randomly distributed inside a disk with the centre at the secondary transmitter (ST). The primary networks (PNs), on the other hand, are not necessarily inside the transmission range of the ST. Two user selection schemes namely, path-loss-based and channel-gain-based approaches, are adopted to serve two SRs according to the power domain NOMA technique. Additionally, the impact of the imperfect hardware impairments (HIs) on both transmitters and receivers is also taken into consideration. Furthermore, a recently proposed transmit power allocation at the ST is employed to concurrently maximize the system performance of the secondary networks (SNs) and to strictly protect the quality-of-service (QoS) of the PNs. The correctness of the developed mathematical frameworks is corroborated by simulation results based on the Monte Carlo method. Finally, we unveil that there is no scheme that always outperforms another. Thus, an adaptive scheme can be proposed to optimize the networks' performance. Our findings also reveal that the outage probability (OP) under the harmful effect of HI can be effectively mitigated by increasing the transmit power of the ST. Additionally, increasing the transmit power of the PT is also beneficial for the SNs with the adopted transmit power allocation at the ST. [ABSTRACT FROM AUTHOR]

Abstract.In this work, we consider a randomized version of the deterministic pseudofractal graph model, which was first proposed in the physical literature. Our model is a sequence of dynamic networks, in which the increment of network size at each time depends on the current size and a sequence of evolving parameters. We first show that the network size is closely related to a branching process in the varying environment. Under a mild assumption, we then prove that the asymptotic degree distribution in our model obeys the power law with an exponent three. Based on this asymptotic degree distribution, we show that the clustering coefficient of our network model converges to a given constant in probability. [ABSTRACT FROM AUTHOR]

In this study we examine the relationship between supply network's topology and its robustness in the presence of random failures and targeted attacks. The agent-based model developed in this paper uses the basic framework and parameters in the experimental game presented in Sterman [1989, Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making context. Management Science, 35 (3), 321-339] for modelling adaptive managerial decision making in an inventory management context. The study extends the linear supply chain context to a complex supply network and undertakes a rigorous examination of robustness of these supply networks that are characterised by distinct network characteristics. We theorise that network characteristics such as average path length, clustering coefficient, size of the largest connected component in the network and the maximum distance between nodes in the largest connected component are related to the robustness of supply networks, and test the research hypotheses using data from several simulation runs. Simulations were carried out using 20 distinct network topologies where 10 of these topologies were generated using preferential attachment approach (based on the theory of scale-free networks) and the remaining 10 topologies were generated using random attachment approach (using random graph theory as a foundation). These 20 supply networks were subjected to random demand and their performances were evaluated by considering varying probabilities of random failures of nodes and targeted attacks on nodes. We also consider the severity of these disruptions by considering the downtime of the affected nodes. Using the data collected from a series of simulation experiments, we test the research hypotheses by means of binomial logistic regression analysis. The results point towards a significant association between network characteristics and supply network robustness assessed using multiple performance measures. We discuss the implications of the study and present directions for future research. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of the switched multi-agent system composed of continuous-time and discrete-time subsystems. Communication among agents is modelled as a random network where the existence of any information channel is probabilistic and independent of other channels. Then, some necessary and sufficient conditions are presented for solving average consensus of the switched multi-agent system under arbitrary switching. Furthermore, we show that the average consensus in different sense (mean square, almost surely and in probability, respectively) are equivalent. Finally, simulations are provided to illustrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

Random graphs with given vertex degrees have been widely used as a model for many real-world complex networks. However, both statistical inference and analytic study of such networks present great challenges. In this article, we propose a new sequential importance sampling method for sampling networks with a given degree sequence. These samples can be used to approximate closely the null distributions of a number of test statistics involved in such networks and provide an accurate estimate of the total number of networks with given vertex degrees. We study the asymptotic behavior of the proposed algorithm and prove that the importance weight remains bounded as the size of the graph grows. This property guarantees that the proposed sampling algorithm can still work efficiently even for large sparse graphs. We apply our method to a range of examples to demonstrate its efficiency in real problems. [ABSTRACT FROM AUTHOR]

This paper presents a non-affine homogenization scheme for materials with a random network microstructure. It is based on a newly developed kinematic constraint that links the microscopic deformation of the network to the macroscopic strain of the material. This relation accounts for the network functionality and is established by means of maximal advance paths that are long enough to reach the macroscopic scales of the continuous body and deform accordingly but are also composed of the microscopic fibres that follow the network deformation. The exact distribution of the variable fibre stretch is determined by the principle of minimum averaged free energy, which ultimately allows one to derive the homogenized elastic response of the network at equilibrium. Besides the general formulation, the model is presented in detail for the case of tetrafunctional networks, for which the micro–macro relation and the expression for the homogenized elastic stress are derived in a compact and interpretable tensorial form. The performance of the model as well as the convexity and stability of the obtained homogenized response of the material is examined for networks composed of two different types of fibres, namely flexible chains and stiff filaments. The qualitative behaviour of the networks predicted for the two considered cases agrees with experimentally observed phenomena for soft materials. This includes a consistent explanation for the difference in the stiffness of elastomers at uniaxial and equibiaxial extension as well as a validation of recent experimental investigations of atypical normal stress amplitudes in biopolymer gels under shear loading. [ABSTRACT FROM AUTHOR]

This article studies the consensus problem for a group of sampled-data general linear dynamical agents over random communication networks. Dynamic output feedback protocols are applied to solve the consensus problem. When the sampling period is sufficiently small, it is shown that as long as the mean topology has globally reachable nodes, the mean square consensus can be achieved by selecting protocol parameters so that n - 1 specified subsystems are simultaneously stabilised. However, when the sampling period is comparatively large, it is revealed that differing from low-order integrator multi-agent systems the consensus problem may be unsolvable. By using the hybrid dynamical system theory, an allowable upper bound of sampling period is further proposed. Two approaches to designing protocols are also provided. Simulations are given to illustrate the validity of the proposed approaches. [ABSTRACT FROM AUTHOR]

The Lotka-Volterra (LV) equations can be used to model the behaviour of complex systems in nature. Trajectories in a stable heteroclinic channel (SHC) describe transient dynamics according to the winnerless competition principle in such a system. The existence of an SHC is guaranteed if the parameters of the LV equations satisfy a number of conditions. We study under what conditions a heteroclinic channel arises in a system where the coupling strengths are chosen randomly. These results describe the overall structure of the system dependent on the length of the channel. This relationship gives an estimation for the possible length of sequences of states in systems occurring in nature. [ABSTRACT FROM AUTHOR]

Two and three dimensional network and continuum models are coupled to evaluate the effective mechanical properties of low-density fibrous composites. Low-density fibrous composites are formed of fibers, binder, and air, and exhibit mechanical properties that strongly depend upon the characteristics of the individual constituents. The effective stiffness and strength are modelled using a fiber network with specific focus on the nature of the joints that connect the individual fibers. In the stiffness calculation, the bonds between fibers are considered rigid, and the primary factors that guide the response of the aggregate are fiber length, diameter, stiffness, and orientation. In strength calculations, the behavior and influence of the binder is more dominant. The model used to predict bulk failure mechanisms is based on both fiber strength and binding node strength. General guidelines and predictions are summarized following a sequence of representative numerical and parametric results. [ABSTRACT FROM AUTHOR]