Complex Heterogeneous Networks Research Articles

Characterizations and Criteria for Synchronization of Heterogeneous Networks to Linear Subspaces

In this paper, we investigate synchronization issues in heterogeneous complex networks. We start with a necessary condition for the existence of synchronous trajectories by analyzing the vector fie...

Trends in Real-time Traffic Simulation

Traffic simulation models and software tools have been developed for the purpose of traffic modelling, planning and to analyse different strategies in traffic control during simulations. Traffic simulation models and tools are increasingly used in real-time for traffic management with the use of area-wide online traffic data. A comparison of 17 simulation software tools has been conducted by analysing scientific papers and technical specifications. An online survey with the focus on realized functionalities and planned improvements has been conducted together with traffic simulation tool developers and product managers. Particular emphasis was placed on the flexibility and adaptively of real-time simulation solutions in the context of heterogeneous road networks (urban, interurban, rural) and other special requirements in non-mainstream regions. It has become apparent that simulation software tools have many challenges in the application of simulating road conditions of complex heterogeneous road transportation networks and heterogeneous traffic with a small amount of real-time data.

A multilayered approach for link prediction in heterogeneous complex networks

Link prediction problem is a difficult task in complex networks due to (i) network size and sparsity, and (ii) extracting efficient similarity measures between node pairs. Although many link predictors have been proposed in the literatures, most of them are presented in homogeneous networks. The heterogeneity of node types adds a new challenge to link prediction problem. A meta-structure known as meta-path has been recently proposed to overcome the heterogeneity challenges. However, defining and generating good meta-paths as well as obtaining high prediction accuracy are still open issues. In this paper, a multilayered approach has been proposed in which a heterogeneous system is modeled as a multilayered complex network. Then, by exploring the network layers with different semantics, a set of meta-paths is generated. Extracting a number of topological features for each meta-path, a number of link predictors is learned which are aggregated to build the final link predictor. The experimental results on a bibliography network (DBLP) show that the proposed approach obtains higher accuracy in comparison with popular heterogeneous proximity measures.

Global dynamics of a network-based SIQRS epidemic model with demographics and vaccination

This paper investigates a new SIQRS epidemic model with demographics and vaccination on complex heterogeneous networks. We analytically derive the basic reproduction number R0, which determines not only the existence of endemic equilibrium but also the global dynamics of the model. The permanence of the disease and the globally asymptotical stability of disease-free equilibrium are proved in detail. By using a monotone iterative technique, we show that the unique endemic equilibrium is globally attractive under certain conditions. Our results really improve and enrich the results in Li et al (2014) [14]. Interestingly, the basic reproduction number R0 bears no relation to the degree-dependent birth, but our simulations indicate that the degree-dependent birth does affect the epidemic dynamics. Furthermore, we find that quarantine plays a more active role than vaccination in controlling the disease.

Physical-layer authentication for wireless security enhancement: current challenges and future developments

While the open nature of radio propagation enables convenient “anywhere” wireless access, it becomes the root of security vulnerabilities in wireless communications. In light of this, physical-layer authentication, which is based on exploitation of the dynamics of physical layer attributes, is emerging as an effective approach to enhancing wireless security. In this article, we first review the existing physical-layer authentication techniques and identify their current limitations, ranging from low authentication reliability to the difficulties of integrating these techniques with the existing wireless infrastructure and applying them in complex future networks. We then present three promising research areas in addressing these challenges. Specifically, we propose the use of the multi-attribute multi-observation technique for enhancing the authentication reliability. In order to apply pointto- point physical-layer authentication techniques into existing wireless networks, we propose a cross-layer authentication approach relying on a composite security key that can seamlessly integrate physical-layer and upper-layer authentication schemes. We also discuss possible ways of invoking physical-layer authentication to reduce both the complexity and latency of the security processes in complex heterogeneous networks with the aid of the proposed physical security context sharing.

Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model

Energy is not only an important international commodity but is also an important financial product. Many studies address the global energy structure by considering energy as commodity, but few study it based on energy’s financial attributes. In this paper, we analyzed the global energy investment structure based on the shareholding relationships between listed energy companies and their shareholders by taking the frontier approach to complex network theory and treating these relationships as a heterogeneous complex network from a global angle. We constructed a primitive investment network of listed energy companies and their shareholders (two sets of actors). Then, based on the two-mode affiliation relationships between two sets of actors and countries, we constructed a derivative investment network of 112 countries (and regions). By calculating different topological features, we quantitatively analyzed the national diversity of outward and inward energy investment, the energy investment preference, the strength of the bilateral energy investment relationship between countries (and regions) and the most powerful group of energy investing countries based on the shareholding relationships of global listed energy companies. It is found that the vast majority of outward and inward investment relationships are still in the hands of a few countries: traditional developed countries, emerging countries, and a few particular island countries (and regions) that are tax havens. This study is helpful for analyzing the roles and investment relationships of nations as well as the global energy investment structure taking the perspective of the energy stock market.

SDN Based User-Centric Framework for Heterogeneous Wireless Networks

Due to the rapid growth of mobile data traffic, more and more basestations and access points (APs) have been densely deployed to provide users with ubiquitous network access, which make current wireless network a complex heterogeneous network (HetNet). However, traditional wireless networks are designed with network-centric approaches where different networks have different quality of service (QoS) strategies and cannot easily cooperate with each other to serve network users. Massive network infrastructures could not assure users perceived network and service quality, which is an indisputable fact. To address this issue, we design a new framework for heterogeneous wireless networks with the principle of user-centricity, refactoring the network from users’ perspective to suffice their requirements and preferences. Different from network-centric approaches, the proposed framework takes advantage of Software Defined Networking (SDN) and virtualization technology, which will bring better perceived services quality for wireless network users. In the proposed user-centric framework, control plane and data plane are decoupled to manage the HetNets in a flexible and coadjutant way, and resource virtualization technology is introduced to abstract physical resources of HetNets into unified virtualized resources. Hence, ubiquitous and undifferentiated network connectivity and QoE (quality of experience) driven fine-grained resource management could be achieved for wireless network users.

Dynamical analysis of a fractional SIR model with birth and death on heterogeneous complex networks

In this paper, a fractional SIR model with birth and death rates on heterogeneous complex networks is proposed. Firstly, we obtain a threshold value R0 based on the existence of endemic equilibrium point E∗, which completely determines the dynamics of the model. Secondly, by using Lyapunov function and Kirchhoff’s matrix tree theorem, the globally asymptotical stability of the disease-free equilibrium point E0 and the endemic equilibrium point E∗ of the model are investigated. That is, when R0<1, the disease-free equilibrium point E0 is globally asymptotically stable and the disease always dies out; when R0>1, the disease-free equilibrium point E0 becomes unstable and in the meantime there exists a unique endemic equilibrium point E∗, which is globally asymptotically stable and the disease is uniformly persistent. Finally, the effects of various immunization schemes are studied and compared. Numerical simulations are given to demonstrate the main results.

EPIDEMIC SPREADING AND GLOBAL STABILITY OF A NEW SIS MODEL WITH DELAY ON HETEROGENEOUS NETWORKS

In this paper, we study a susceptible-infected-susceptible (SIS) model with time delay on complex heterogeneous networks. Here, the delay describes the incubation period in the vector population. We calculate the epidemic threshold by using a Lyapunov functional and some analytical methods, and find that adding delay increases the epidemic threshold. Then, we prove the global stability of disease-free and endemic equilibria by using the theory of functional differential equations. Furthermore, we show numerically that the epidemic threshold of the new model may change along with other factors, such as the infectivity function, the heterogeneity of the network, and the degrees of nodes. Finally, we find numerically that the delay can affect the convergence speed at which the disease reaches equilibria.

Dynamic analysis of an SEIRS model with nonlinear infectivity on complex networks

In this paper, we study the spreading of infections on complex heterogeneous networks based on an SEIRS epidemic model with nonlinear infectivity. By mathematical analysis, the basic reproduction number R0 is obtained. When R0 is less than one, the disease-free equilibrium is globally asymptotically stable and the disease dies out, while R0 is greater than one, the disease-free equilibrium becomes unstable and the disease is permanent, and in the meantime there exists a unique endemic equilibrium which is globally attractive under certain conditions. Finally, the effects of various immunization schemes are studied. To verify our theoretical results, the corresponding numerical simulations are also included.

Improved joint radio resource management usage grey fuzzy control in heterogeneous wireless networks

This paper presents an innovative approach to joint radio resource management (JRRM) over a complex heterogeneous network by using several realistic radio access technologies (RATs). This work applies a novel grey fuzzy control process to joint radio resource management (JRRM). In this architecture, the JRRM techniques are divided between grey fuzzy control (GFC) and grey fuzzy multi-attribute decision making (GFMADM). These enhancements lead to a new JRRM architecture which seeks to minimize the multi-user interference, decrease blocking, lower dropping probabilities and improve load balancing. The policy uses the signal strength, path loss, and load algorithms for admission control (AC), horizontal handoff (HHO) and vertical handover (VHO) in a heterogeneous wireless scenario which includes GSM/ EDGE radio access network (GERAN), high-speed packet access (HSPA) and long term evolution (LTE) RATs. Our simulation results show that the proposed GFC/GFMADM algorithm can decreases multi-user interference which results in the increasing system radio capacity, decreasing blocking and dropping probabilities and enhancing load balancing.

The dynamics of an HBV epidemic model on complex heterogeneous networks

In this paper, an HBV epidemic model on complex heterogeneous networks is proposed. Theoretical analysis of the HBV spreading dynamics is presented via mean-field approximation. Stabilities of the disease-free equilibrium and the endemic equilibrium are studied. The theoretical results reveal that disease propagation is impacted by the heterogeneous connectivity patterns and the underlying network structures.

Bounded synchronization of a heterogeneous complex switched network

This paper investigates synchronization issues of a heterogeneous complex network with a general switching topology in the sense of boundedness, when no complete synchronization manifold exists. Several sufficient conditions are established with the Lyapunov method and the differential analysis of convergence to determine the existence and estimate the convergence domain for the local and global bounded synchronization of a heterogeneous complex network. By using the consensus convergence of a switched linear system associated with the switching topology, explicit bounds of the maximum deviation between nodes are obtained in the form of a scalar inequality involving the property of the consensus convergence, the homogeneous and heterogeneous dynamics of individual nodes for the local and global cases. These analytical results are simple yet generic, which can be used to explore synchronization issues of various complex networks. Finally, a numerical simulation illustrates their effectiveness.

Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

In this paper, we propose a novel two-stage rumor spreading Susceptible-Infected-Authoritative-Removed (SIAR) model for complex homogeneous and heterogeneous networks. The interaction Markov chains (IMC) mean-field equations based on the SIAR model are derived to describe the dynamic interaction between the rumors and authoritative information. We use a Monte Carlo simulation method to characterize the dynamics of the Susceptible-Infected-Removed (SIR) and SIAR models, showing that the SIAR model with consideration of authoritative information gives a more realistic description of propagation features of rumors than the SIR model. The simulation results demonstrate that the critical threshold λc of the SIAR model has the tiniest increase than the threshold of SIR model. The sooner the authoritative information is introduced, the less negative impact the rumors will bring. We also get the result that heterogeneous networks are more prone to the spreading of rumors. Additionally, the inhibition of rumor spreading, as one of the characteristics of the new SIAR model itself, is instructive for later studies on the rumor spreading models and the controlling strategies.

Context Monitoring Optimization in Autonomic Networks

The recent advances in network management systems suggest the adoption of autonomic mechanisms in order to minimize the need for human intervention while handling complex heterogeneous networks. Data acquisition performed by monitoring processes is an essential part of autonomic mechanisms. The rate of sampling is a crucial factor since it is related to (1) the successful/unsuccessful detection of events, (2) the processing power needed to perform the sampling and (3) the energy that a node consumes during such actions. In order to address these issues we designed a simple and efficient mechanism that dynamically adapts the sampling rate of the context monitoring procedure. The merits of the mechanism are quantified by means of an analytical model as well as through extensive simulations that validated the theoretic outcomes. Finally, we experimentally assessed the effectiveness and efficiency of our approach through two real-world experiments. Overall results showcase that our mechanism achieves high detection rates while in parallel minimizes significantly the number of monitoring loops thus, emerges as a viable approach for context monitoring optimization in autonomic networks.

Quasi-synchronization of Markovian jump complex heterogeneous networks with partly unknown transition rates

This paper is concerned with the synchronization problem for a class of Markovian jump complex heterogeneous networks with partly unknown transition rates and time-varying delay. Based on the concept of quasi-synchronization, a novel stochastic Lyapunov functional is constructed to solve the problem. Then sufficient quasi-synchronization conditions are presented, and explicit expressions of error levels are proposed to estimate the synchronization error. Finally, numerical examples are provided to demonstrate the feasibility and effectiveness of the proposed theoretic result.

Topological interactive analysis of power system and its communication module: A complex network approach

Power system and its communication system, which can be called a cyber-physical system, are interconnected and interdependent on each other. This paper considers the interaction problem between power system and its communication module from the perspective of the topological structure. Firstly, some structural properties and centrality measures of complex networks are briefly reviewed. Furthermore, novel interactive measures are proposed to describe the interactive system in terms of topologies. Finally, based on these metrics, the statistical properties and the interactive relationships of the main power system and its communication module (abstracted as two complex heterogeneous networks) of one province in China are investigated.

Lumping Izhikevich neurons

We present the construction of a planar vector field that yields the firing rate of a bursting Izhikevich neuron can be read out, while leaving the sub-threshold behavior intact. This planar vector field is used to derive lumped formulations of two complex heterogeneous networks of bursting Izhikevich neurons. In both cases, the lumped model is compared with the spiking network. There is excellent agreement in terms of duration and number of action potentials within the bursts, but there is a slight mismatch of the burst frequency. The lumped model accurately accounts for both intrinsic bursting and post inhibitory rebound potentials in the neuron model, features which are absent in prevalent neural mass models.

A new regime for highly robust gamma oscillation with co-exist of accurate and weak synchronization in excitatory-inhibitory networks.

A great number of biological experiments show that gamma oscillation occurs in many brain areas after the presentation of stimulus. The neural systems in these brain areas are highly heterogeneous. Specifically, the neurons and synapses in these neural systems are diversified; the external inputs and parameters of these neurons and synapses are heterogeneous. How the gamma oscillation generated in such highly heterogeneous networks remains a challenging problem. Aiming at this problem, a highly heterogeneous complex network model that takes account of many aspects of real neural circuits was constructed. The network model consists of excitatory neurons and fast spiking interneurons, has three types of synapses (GABAA, AMPA, and NMDA), and has highly heterogeneous external drive currents. We found a new regime for robust gamma oscillation, i.e. the oscillation in inhibitory neurons is rather accurate but the oscillation in excitatory neurons is weak, in such highly heterogeneous neural networks. We also found that the mechanism of the oscillation is a mixture of interneuron gamma (ING) and pyramidal-interneuron gamma (PING). We explained the mixture ING and PING mechanism in a consistent-way by a compound post-synaptic current, which has a slowly rising-excitatory stage and a sharp decreasing-inhibitory stage.

Data Reduction and Energy Sustenance in Multisensor Networks for Landslide Monitoring

We present extensive studies into data reduction and energy minimization in Sensor Networks for landslide detection. What started as a simple homogenous network of rainfall sensors has evolved into a complex heterogeneous network of 20 wireless probes with each probe consisting of four different types of sensors to measure rainfall, moisture, pore pressure, and movement, all of which have been in continuous operation for more than two years in the equatorial forests of Kerala, India. Each probe runs on solar power and the frequency of sensor data measurements from the probes is dynamically and adaptively throttled in real time, based on climatic conditions to minimize the total energy consumption. The probes also work together to identify who among them is sensing the maximum parameter, after which all other sensors are switched off for a predetermined duration. We present detailed analysis of data reduction and energy savings, and also relate them to the effectiveness of landslide detection. The whole system was validated by the issuance of a landslide warning in the month of July (last monsoon season), which facilitated pre-emptive action by the local government and community to prevent loss of human life.