Complex Heterogeneous Networks Research Articles

Dynamical analysis of rumor spreading model in multi-lingual environment and heterogeneous complex networks

A novel rumor propagation model with education mechanism is firstly proposed in multi-lingual environment and heterogeneous networks. Firstly, the threshold behaviour and dynamics of model are examined through analyzing the acquired differential equations. Next, based on Routh-Hurwitz judgment, Lyapunov method and LaSalle’s invariance principle, the existence and stability of rumor-free/spreading equilibrium points (RFEP/RSEP) are carried out. Furthermore, we found that the educational mechanism has significance influence on rumor spreading through sensitivity analysis. Therefore, the optimal control is presented to optimize the educational mechanism. Additionally, numerical simulations is given to show the correctness of theoretical results. Finally, combine with practice, a model application is presented to verify the effectiveness of the established model and the proposed optimal control.

Global dynamics of an epidemic model with incomplete recovery in a complex network

In this work, we study the global dynamics of a new SIRI epidemic model with demographics, graded cure and relapse in a complex heterogeneous network. First, we analytically make out the epidemic threshold R0 which strictly depends on the topology of the underlying network and the model parameters. Second, we show that R0 plays the role of a necessary and sufficient condition between extinction and permanence of the disease. More specifically, by using new Lyapunov functions, we establish that the disease free-equilibrium state E0 is globally asymptotically stable when R0≤1, otherwise we proved the existence and uniqueness of the endemic state E*. Then, we show that E* is globally asymptotically stable. Finally, we present a series of numerical simulations to confirm the correctness of the established analytical results.

Model-based exception mining for object-relational data

This paper develops model-based exception mining and outlier detection for the case of object-relational data. Object-relational data represent a complex heterogeneous network, which comprises objects of different types, links among these objects, also of different types, and attributes of these links. We follow the well-established exceptional model mining (EMM) framework, which has been previously applied for subgroup discovery in propositional data; our novel contribution is to develop EMM for relational data. EMM leverages machine learning models for exception mining: An object is exceptional to the extent that a model learned for the object data differs from a model learned for the general population. In relational data, EMM can therefore be used for detecting single outlier or exceptional objects. We combine EMM with state-of-the-art statistical-relational model discovery methods for constructing a graphical model (Bayesian network), that compactly represents probabilistic associations in the data. We investigate several outlierness metrics, based on the learned object-relational model, that quantify the extent to which the association pattern of a potential outlier object deviates from that of the whole population. Our method is validated on synthetic data sets and on real-world data sets about soccer and hockey matches, IMDb movies and mutagenic compounds. Compared to baseline methods, the EMM approach achieved the best detection accuracy when combined with a novel outlinerness metric. An empirical evaluation on soccer and movie data shows a strong correlation between our novel outlierness metric and success metrics: Individuals that our metric marks out as unusual tend to have unusual success.

Plant Cell Walls: Impact on Nutrient Bioaccessibility and Digestibility.

Cell walls are important structural components of plants, affecting both the bioaccessibility and subsequent digestibility of the nutrients that plant-based foods contain. These supramolecular structures are composed of complex heterogeneous networks primarily consisting of cellulose, and hemicellulosic and pectic polysaccharides. The composition and organization of these different polysaccharides vary depending on the type of plant tissue, imparting them with specific physicochemical properties. These properties dictate how the cell walls behave in the human gastrointestinal tract, and how amenable they are to digestion, thereby modulating nutrient release from the plant tissue. This short narrative review presents an overview of our current knowledge on cell walls and how they impact nutrient bioaccessibility and digestibility. Some of the most relevant methods currently used to characterize the food matrix and the cell walls are also described.

Quasi‐synchronization of multilayer heterogeneous networks with a dynamic leader

SummaryThis article considers the pinning quasi‐synchronization problem for a class of leader‐following multilayer heterogeneous complex networks. The leader has nonzero control inputs that are unavailable to the followers. Multilayer heterogeneous networks with heterogeneous node dynamics and two different layer structures are proposed in this article. Two types of discontinuous coupling laws are designed for achieving synchronization in multilayer heterogeneous networks. Based on Lyapunov stability theory and the matrix theory, some sufficient criteria for pinning quasi‐synchronization are derived, and the upper bound of quasi‐synchronization errors is solved. Finally, numerical simulations are performed to illustrate the effectiveness of the theoretical results.

Finite-Time Synchronization for Heterogeneous Complex Networks with Time-Varying Delays

This paper studies the problem of finite-time synchronization for a class of heterogeneous complex networks which not only have node time-varying delays and coupled time-varying delays but also contain uncertain disturbance. An appropriate controller is designed such that this type of network can be synchronized within a finite time. By constructing a proper Lyapunov function and using the finite-time stability theory, the sufficient conditions for the network to achieve finite-time synchronization are given and the finite time is estimated. Finally, the conclusions obtained are extended to the case of homogeneous complex networks with time-varying delays and uncertain disturbance.

Particle Swarm-Based Cell Range Expansion for Heterogeneous Mobile Networks

Traffic demand in wireless communication systems has emerged as a key issue over recent decades. An ever-increasing trend is projected for the next few years, with explosive data traffic expected to materialize and mobile users imposing new quality of service requirements. This growing traffic demand, combined with increasingly complex heterogeneous network (HetNet) scenarios, has presented ever more challenges for mobile network operators in terms of service, coverage, load balancing, and quality of service. Considering the traditional association mechanism based on maximum power received, HetNets tend to remain unbalanced, making it challenging to satisfy mobile users' traffic requirements. In this paper, instead of trying to maximize the achievable downlink rate per user, we couple the cell range expansion (CRE) technique with a particle swarm optimization (PSO) algorithm to maximize the number of users whose downlink requirements are met. The proposed scheme considers both the loads of base stations and the signal-to-interference-plus-noise ratio (SINR) of user equipment to model an objective function that seeks to compute specific CRE bias values per small base station. The proposed scheme is also compared with some classical PSO implementations. Numerical results validate the performance of the proposed schemes, which effectively fulfill users' data traffic requirements by reducing network imbalance.

Finite-time synchronization of delayed fractional-order heterogeneous complex networks

This paper is devoted to exploring the finite-time (FET) synchronization problem of time-varying delay fractional-order (FO) coupled heterogeneous complex networks (TFCHCNs) with external interference via a discontinuous feedback controller. Firstly, we propose a novel Lemma which is useful for discussing the FET stability and synchronization problem of FO systems. Secondly, based on the proposed Lemma, a discontinuous feedback controller is designed to guarantee the FET synchronization of TFCHCNs with external interference. Moreover, the upper bound of settling-time function is obtained. Finally, two simulation examples are provided to verify the practicability of our findings.

The impact of information spreading on epidemic vaccination game dynamics in a heterogeneous complex network- A theoretical approach

Abstract A modified susceptible-vaccinated-infected-recovered (SIR/V) with unaware-aware (UA) epidemic model in heterogeneous networks is presented to study the effect of information spreading in the spatial structure of the vaccination game on epidemic dynamics. Two layers SIR/V epidemic model is considered to elucidate information spreading, where the fraction of susceptible, vaccinated and infected individuals are parted as unaware and aware state as each susceptible and vaccinated persons are allied with their infected neighbors by a spatial structure, say, an underlying network. The context deduces epidemic vaccination game with awareness influence dynamics in one single season followed by a strategy update process that refer an individual to take imperfect vaccination or not. We considered two different strategy updating rules: individual based risk assessment (IB-RA) and strategy-based risk assessment (SB-RA) to explore how different underlying network topologies, say, random graph and scale free networks, subsequently giving impact on the final epidemic size, vaccination coverage and average social payoff through the effect of information spreading on epidemic. Thus, it can be seen that, awareness can enhance the epidemic threshold effectiveness and lessen the spreading of infection in a scale free network other than random graph and homogeneous network.

Predicting drug-disease associations with heterogeneous network embedding.

The prediction of drug-disease associations holds great potential for precision medicine in the era of big data and is important for the identification of new indications for existing drugs. The associations between drugs and diseases can be regarded as a complex heterogeneous network with multiple types of nodes and links. In this paper, we propose a method, namely HED (Heterogeneous network Embedding for Drug-disease association), to predict potential associations between drugs and diseases based on a drug-disease heterogeneous network. Specifically, with the heterogeneous network constructed from known drug-disease associations, HED employs network embedding to characterize drug-disease associations and then trains a classifier to predict novel potential drug-disease associations. The results on two real datasets show that HED outperforms existing popular approaches. Furthermore, some of our predictions have been verified by evidence from literature. For instance, carvedilol, a drug that was originally used for heart failure, left ventricular dysfunction, and hypertension, is predicted to be useful for atrial fibrillation by HED, which is supported by clinical trials.

Fractal-Based Reliability Measure for Heterogeneous Manufacturing Networks

Nowadays, production-oriented manufacturing is transforming to service-oriented one, which leads to an increasing demand for high reliability of manufacturing systems. However, a popular approach to measuring terminal reliability of complex networked systems is based on graph theory, which has been shown to be an NP-hard problem. Though the NP-hard problem can be avoided by employing a statistical measure method for terminal reliability of random networks based on percolation theory, there is still lack of a general assessment approach to calculating terminal reliability of heterogeneous complex networks in intelligent manufacturing. In this paper, we propose a novel fractal-based approach to measuring the terminal reliability of heterogeneous networks. With help of the renormalization procedure that coarse grains a network into boxes containing nodes within given lateral size and inverse renormalization, which gives a fractal network growth model, a fractal network approximation of an arbitrary complex network is obtained. This fractal network topology can be described by a superposition of fractal elements based on fractal theory. Following this description, terminal reliability is the function of reliability of fractal elements. Then, a reliability assessment algorithm with computational complexity $O(N^2)$ based on fractal elements is developed. Numerical simulation is performed on a real network and a fractal network to validate the effectiveness of our method.

SHVC CU Processing Aided by a Feedforward Neural Network

The development of multimedia and hardware technologies has led to a great number of industrial video applications, such as virtual reality, high-definition video surveillance, and remote monitoring. As complex communication environments and heterogeneous networks are common in industrial applications, industrial videos are required to support a diverse range of display resolutions and transmission channel capacities. Scalable high-efficiency video coding (SHVC) standards provide the tools to meet this requirement. However, it is highly computationally expensive. Coding complexity has a great impact on SHVC performance in industrial applications. Many of these applications are sensitive to time delay and have limited power. Thus, improvements are required to ensure the practical usability of SHVC encoders. In SHVC encoders, intra/interprediction of variable coding unit (CU) sizes is independently performed for the base and enhancement layers (ELs). There are many interlayer similarities that can be exploited to speed up the procedure for EL coding. In this paper, we propose a feedforward neural network aided model for CU size and mode decisions for SHVC, which utilizes base layer coding information and the coding data of spatiotemporal neighboring CUs to decide which CU sizes or prediction modes can be bypassed for certain EL CUs. Two feedforward neural network based learning models are built for CU classification, which are introduced in the procedures for CU size and mode decisions, respectively. According to the analysis from a large number of video sequences, the representative features are directly extracted from the coding information of previously coded neighboring CUs to avoid computational overheads. After the training is finished, these two models are designed and integrated to build classifiers. Then, two online classification approaches are designed for the CU size and mode decision procedures to classify each CU's type. Finally, different candidate CU sizes and prediction modes are adaptively assigned for each type of CU. This approach outperforms the state-of-the-art fast SHVC/high-efficiency video coding (HEVC) algorithms with approximately 19–42% coding time savings or better compression efficiency, which will be beneficial for the realization of real-time scalable video coding.

MATHEMATICAL POWER FLOW MODEL IN AN ELECTRICAL SYSTEM CONTAINING A SERIAL COMPENSATOR THRISTOR CONTROLLED REACTIVE COMPONENT

A unified mathematical model of the power flow in a system containing a reactive component compensator consisting of capacitor banks connected in series to a thyristor control reactor is presented. The application of the FACTS (Flexible Alternative Current Transmission System - Controlled flexible DC power transmission) technology is shown to reduce the gap between the controlled and unmanaged modes of operation of the electric power system (EPS), presenting dispatching personnel with additional degrees of freedom in the management of power flows and voltages in excess and deficit areas of the electric network. The main objectives of the FACTS technology application are studied: increasing the transmission line capacity to the thermal limit; optimizing power flows in a complex heterogeneous network; increasing the static and dynamic stability of the electric power system. To assess the action of the new generation of regulators of the power system, two alternative models of power flow in the electric power system are considered. In the first model, the concept of alternating series reactance is used as a state variable. In the second model, the characteristic of the advance angle is used, given in the form of a nonlinear dependence in the problem of calculating the power flow using the Newton-Raphson method. Conclusions are made on the presented models of power flow allowing to estimate possibilities of the serial capacitor with thyristor control TCSC (Thyristor Controlled Series Capacitor), as FACTS device, on improvement of modes of functioning of electric power system.

Research on Functional Robustness of Heterogeneous Combat Networks

The combat system-of-systems in high-tech informative warfare consists of different types of combat systems connected via multiple interconnections, which can be abstracted as types of complex heterogeneous networks. Research on the functional robustness of heterogeneous combat networks (HCNs) is of significant military value for optimizing operation process planning and for improving network survivability in complex electromagnetic battle fields. This study presents an integrated framework, known as the HCN functional robustness (HCNFR) framework to measure the functional robustness of HCNs under various attacking strategies. Specifically, a heterogeneous network model is introduced to characterize HCNs considering different types of functional entities and information flows. Based on this setup, the rescaled operational capability index (ROCI) is first proposed to evaluate the functional robustness of HCNs, and the criterion for functional disintegration of HCNs is established by summing the values of ROCI during the attacking process. Finally, the reliability and effectiveness of HCNFR are illustrated with an empirical case study. The results provide useful insights for operational guidance and design of a more functional and robust combat system-of-systems.

Analysis of Policy-Based Security Management System in Software-Defined Networks

In software-defined networks, policy-based security management or architecture (PbSA) is an ideal way to dynamically control the network. We observe that on the one hand, this enables security capabilities intelligently and enhance fine-grained control over end user behavior. But, on the other hand, dynamic variations in network, rapid increases in security attacks, geographical distribution of nodes, complex heterogeneous networks, and so on have serious effects on the performance of PbSAs. These affect the flow specific quality of service requirements with further degradation of the performance of the security context. Hence, in this letter, PbSA’s performance is evaluated. The key factors including a number of rules, rule-table size, position of rules, flow arrival rate, and CPU utilization are examined, and found to have considerable impact on the performance of PbSAs.

Stochastic distribution synchronization and pinning control for complex heterogeneous dynamical networks

AbstractThis paper investigates the stochastic synchronization and pinning control in the sense of probability distribution for a general model of complex heterogeneous dynamical networks subjected to stochastic disturbances. Some generic stochastic synchronization criteria are established for both cases of undirected and directed topology by using the ergodic theory on stochastic dynamical systems. Compared with most existing studies on the stochastic synchronization in the sense of mean square, it is demonstrated that the concept of stochastic distribution synchronization can well characterize the realistic structure and essential nature of complex practical stochastic systems. Subsequently, two representative examples of complex heterogeneous dynamical networks, namely coupled stochastic Duffing oscillators and coupled FitzHugh‐Nagumo neuron oscillators, are given to illustrate and numerically verify the theoretical results.

Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network

As a particular type of social networking site, online academic communities have revolutionized the way researchers collaborate and communicate with each other. Accompanying the growth in the number of users registered on online academic communities, the information overload problem presents a great challenge for researchers trying to find relevant and reliable friends there. Different from friends in conventional social networking sites, friends in online academic communities are denoted as scholar-friends in this research. Scholar-friend recommendation in online academic communities involves different entities (e.g., researchers, research articles, affiliations, research interests, and status updates) and various relationships among entities (e.g., scholar-friend relationship of researchers, post relationship between researchers and status updates, and writing relationship between researchers and papers), which constitute a complex heterogeneous network. By leveraging the entity and relationship data in online academic communities, this research proposes a heterogeneous network-based approach to recommending scholar-friends where information gain is used to identify valuable meta paths and a regularization-based optimization is employed to make personalized recommendations for each individual researcher. Experimental results based on historical data and user experiments demonstrate that the proposed approach can achieve better performance compared to some baseline approaches. Additionally, we further discuss how the meta paths and corresponding learned weights can help to understand researchers' preferences and behaviors.

Global dynamics of an SIRS model with demographics and transfer from infectious to susceptible on heterogeneous networks.

In this paper, by taking full consideration of demographics, transfer from infectious to sus-ceptible and contact heterogeneity of the individuals, we construct an improved Susceptible-Infected-Removed-Susceptible (SIRS) epidemic model on complex heterogeneous networks. Using the next generation matrix method, we obtain the basic reproduction number $\mathcal{R}_0$ which is a critical value and used to measure the dynamics of epidemic diseases. More specifically, if $\mathcal{R}_0$ < 1, then the disease-free equilibrium is globally asymptotically stable; if $\mathcal{R}_0$ > 1, then there exists a unique endemic equilib-rium and the permanence of the disease is shown in detail. By constructing an appropriate Lyapunov function, the global stability of the endemic equilibrium is proved as well under some conditions. Moreover, the effects of three major immunization strategies are investigated. Finally, some numerical simulations are carried out to demonstrate the correctness and validness of the theoretical results.

Event-Based Synchronization of Heterogeneous Complex Networks Subject to Transmission Delays

In this paper, the problem of event-based synchronization of heterogeneous complex networks is investigated. Specifically, the influence of transmission delays on event-based synchronization is considered. The designed distributed controller for each nonidentical node in heterogeneous network includes reference generator (RG) and robust regulator. Event-based communication protocol is utilized to ensure the synchronization among the identical RGs and target leader. Meanwhile, the interevent intervals are lower bounded by a positive constant. Furthermore, the proposed high-gain output feedback and adaptive control law guarantee that all nonidentical nodes converge to their RGs. Based on these two steps, which are coincided with the separation principle, nonidentical nodes can track the target leader. In addition, theoretical results are demonstrated by a numerical example.

Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.