Presents the table of contents for this issue of the publication.

Presents the table of contents for this issue of the publication.

The papers in this special section focus on the economics on modern network systems. Many modern networks are becoming increasingly heterogeneous, dynamic, and complex. The need for smart and self-organizing network designs has become a central research issue in a variety of applications and scenarios. Proper economic mechanism design will go hand-in-hand with technology advances in solving many complex

The papers in this special section focus on the network of cyber-social networks (NCSN), with particular emphasis on modeling, analysis and control. NCSN is a promising new area that has recently attracted significant interests. Its core differentiator is the tight conjoining among heterogeneous cyber-social networks or between cyber and physical networks. The focus of this special section is to address

The papers in this special section focus on network science for high confidence cyber-physical systems (CPS) Here CPS refers to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. To enable high-confidence CPS for achieving better benefits as well as supporting emerging applications, network science-based

The papers in this special section focus on scalability and privacy in online social network services. (OSNs) The growing popularity of OSNs and their emerging applications attracted much attention from both academia and industry during recent years. Due to their nature, social networks are considered as sources of Big Data containing large amounts of privacy-sensitive information. A social network

The aim of this work is to evaluate the impact of node churn –nodes leaving and rejoining the network– on the Bitcoin network. We provide a comprehensive analytical model for the churning process. We use a Continuous Time Markov Chain (CTMC) to describe the behavior of a node, and then apply the results to model the changes in connectivity and the impact on network performance. We analyze the time

This paper proposes a new distributed real-time optimization for MapReduce-style framework in emerging cloud platforms supporting dynamic speed scaling functions. Distinctively different from the existing MapReduce parallelism strategy with fixed specific data chuck sizes, the new approach is able to dynamically dispatch input data of adequate sizes and synthesize interim processing results according

One popular model for network analysis is the exchangeable graph model (ExGM), which is characterized by a two-dimensional function known as a graphon . Estimating an underlying graphon becomes the key of such analysis. Several nonparametric estimation methods have been proposed, and some are provably consistent. However, if certain useful features of the nodes (e.g., age and schools in a social network

The critical node detection based on cascade model is a very important way for analyzing network vulnerability and has recently attracted the attention of many researchers in complex network area. Most of existing works aim to design effective attack strategies which lead to maximal damage to the network (i.e. the destructiveness of the attack), while the number of initial attacked nodes (i.e. $k$

Detecting or recognizing event related change points in dynamic networks becomes an increasingly important task, as a change in network's structure may associate with a change in function of the networked system. However, general change point detection methods either fail to extract effective features or do not scale well. In this work, we introduce the probability distribution of nodes’ importance

In smart grid, the highly dynamics of electrical loads introduce uncertainty to the system, which also brings new challenges for the power grid control and optimization. Reporting demand information by customers in solving the economic dispatch control (EDC) problem in power grids is a promising approach, but it raises important privacy concerns. One solution is to add random noise to aggregate queries

The curse of dimensionality is a challenging issue in network science: the problem of inferring the network structure from sparse and noisy data becomes more and more difficult, indeed, as their dimensionality increases. We here develop a general strategy for dimensional reduction using iteratively thresholded ridge regression screener, one statistical method aiming to resolve the problem of variable

While there has been considerable work addressing consensus and group consensus in single-layer networks, not much attention has been devoted to consensus in multilayer networks. In this paper, we fill this gap by considering multilayer networks consisting of agents of different types while agents of the same type are arranged in separate layers. The patterns of emerging group consensus are determined

An important question that often arises in the operation of networked systems is whether to collect the real-time data or to estimate them based on the previously collected data. Various factors should be taken into account such as how informative the data are at each time instant for state estimation, how costly and credible the collected data are, and how rapidly the data vary with time. The above

In this paper, the economic dispatch problem (EDP) in smart grids is investigated over a directed network, which concentrates on allocating the generation power among the generators to satisfy the load demands with minimal total generation cost while complying with all constraints of local generation capacity. Each generator possesses its own local generation cost, and the total generation cost is

Air transportation networks develop independently based on the needs of carriers, economic and political factors, as well as their interactions. A deeper study of these networks can provide insights into the growing process and the characteristics of this multi-billion dollar industry. In this regard, reliable network- formation models are required. Generating synthetic models for airline transportation

Coupon advertisement is everywhere in people's daily lives, and it is a common marketing strategy adopted by merchants. A problem, Budget Profit Maximization with Coupon Advertisement, is formulated in this article, which is a branch of classical profit maximization problem in social networks. Profit maximization has been researched intensively before, but its theoretical bound is not satisfactory

This paper investigates the finite-time synchronization (FTS) problem for a kind of nonlinear multi-agent systems by exploiting the finite-time passivity (FTP). Firstly, with the help of designed state feedback controller and the adaptive state feedback controller, two sufficient conditions for ensuring the FTP of single agent are proposed. Moreover, we prove that finite-time passive agent is finite-time

In a networked system, functionality can be seriously endangered when nodes are infected , due to either internal random failures or a contagious virus that develops into an epidemic. Given a snapshot of the network representing the nodes’ states (infected or healthy), infection analysis refers to distinguishing an epidemic from random failures and gathering information for effective countermeasure

In this work, we consider the coordination control of second-order multi-agent systems subject to both velocity and input constraints with only relative output information. First, the leaderless consensus problem is considered where a nonlinear distributed controller is proposed which achieves asymptotic consensus of the agents using only local velocity and relative output information. Then, generalization

Quite often real-world networks can be thought of as being symmetric, in the abstract sense that vertices can be found to have similar or equivalent structural roles. However, traditional measures of symmetry in graphs are based on their automorphism groups, which do not account for the similarity of local structures. We introduce the concept of local symmetry , which reflects the structural equivalence

So far there are still limited results on the role of network topology matrix in controlling complex networks with minimum control cost. In this article, we study this problem and propose an optimization model by considering the topology matrix as a variable to minimize the control cost. As sparsity nature commonly exists in many real-world networks, sparse, and non-negative constraints are included

Understanding the evolution of collective beliefs is of critical importance to get insights on the political trends as well as on social tastes and opinions. In particular, it is pivotal to develop analytical models that can predict the beliefs dynamics and capture the interdependence of opinions on different subjects. In this article we tackle this issue also accounting for the individual endogenous

In this article, we consider the optimal design of networked estimators to minimize the communication/measurement cost under the networked observability constraint. This problem is known as the minimum-cost networked estimation problem, which is generally claimed to be NP-hard. The main contribution of this work is to provide a polynomial-order solution for this problem under the constraint that the

Reactive jamming attack, enabled by programmable software defined radio, has become a serious threat to the ubiquitous wireless systems. Wireless scheduling under reactive jamming attack is important yet challenging. In this article, we consider the utility optimal scheduling problem for multi-hop wireless networks under reactive jamming attack. We propose pilot-aided channel probing , defensive power

In order to make intelligent transportation systems (ITSs) come true, execution of a large amount of data needs to be migrated from the cloud centers to the edge nodes, especially in the scenarios requiring ultra reliable low latency communications (URLLC). In this article, we propose to study the energy-aware task allocation problem in the vehicular fog networks considering URLLC. Specifically, a

Cooperative computation among peer mobile devices via device-to-device (D2D) links, a.k.a. D2D offloading, is a promising technology to enhance mobile computing performance and reduce core wireless network traffic. However, D2D offloading also creates new security risks as malware can relatively easily compromise mobile devices participating in D2D offloading and propagate across the entire network

This article addresses the problem of consensus in networks divided into subnetworks (also called clusters), where each node of the network graph represents an agent with linear dynamics. Each subnetwork is represented by a directed graph. Moreover, the agents in each cluster cannot communicate with agents from other clusters, except one single agent of each subnetwork, which is called a leader. These

Edge networking has become an important technology for providing low-latency services to end users. However, deploying an edge network does not guarantee continuous service for mobile users. Mobility can cause frequent interruptions and network delays as users leave the initial serving edge. In this paper, we propose a solution to provide transparent service continuity for mobile users in large-scale

The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player (i.e., the decision maker) here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many

This article deals with fixed-time passification problem of interconnected networks composed of multiple memristive reaction-diffusion subsystems. Different from the finite-time passivity proposed by Wang, Zhang, et al. (2018), a novel concept of fixed-time passivity/passification is proposed by using upper right Dini derivative, where the settling time is independent on initial value. Next, by designing

This article is devoted to discussing the exponential synchronization for a kind of delay derivative coupled neural networks with stochastic disturbance and multiple time-varying delays. To simulate more practical situations and widen the synchronization application fields in network science, the coupled neural networks with cluster-tree topology structure is studied by applying a novel impulsive pinning

The spread of new products in a networked population is often modeled as an epidemic. However, in the case of “complex” contagion, these models do not capture nuanced, dynamic social reinforcement effects in adoption behavior. In this paper, we investigate a model of complex contagion which allows a coevolutionary interplay between adoption, modeled as an SIS epidemic spreading process, and social

In mobile wireless sensor networks (MWSNs), because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, network design in fact indicates that there are

We introduce a cost-benefit model for network formation, where exchanges among nodes are based on reciprocity. Peers receive from the network an amount of resources commensurate with their contribution. When creating links is costly, peers tend to limit the number of their connections, and sparsity of the exchange network may be desirable. In a fully connected graph, finding the sparsest exchanges

Link prediction can discover missing information and evolution mechanism of complex networks, so a huge number of novel algorithms have been proposed recently. However, the existing link prediction algorithms for directed signed networks only depend on motifs that satisfy status theory, and other types of motifs are rarely taken into account. In this study, first we propose a link prediction method

We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks

Two of the most common pattern formation mechanisms are Turing-patterning in reaction-diffusion systems and lateral inhibition of neighboring cells. In this paper, we introduce a broad dynamical model of interconnected modules to study the emergence of patterns, with the above mentioned two mechanisms as special cases. Our results do not restrict the number of modules or their complexity, allow multiple

Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely

A colored graph is a directed graph in which nodes or edges have been assigned colors that are not necessarily unique. Observability problems in such graphs consider whether an agent observing the colors of edges or nodes traversed on a path in the graph can determine which node they are at currently or which nodes were visited earlier in the traversal. Previous research efforts have identified several

We investigate the widely encountered problem of detecting communities in multiplex networks, such as social networks, with an unknown arbitrary heterogeneous structure. To improve detectability, we propose a generative model that leverages the multiplicity of a single community in multiple layers, with no prior assumption on the relation of communities among different layers. Our model relies on a

Studies show that real-world user mobility has certain spatio-temporal correlations. However, most of the existing works on device-to-device multicast (D2MD) networks use randomly generated user locations, thus fail to capture the real-world spatio-temporal behavior of the users. In this work, we propose a realistic performance evaluation of the D2MD network that takes into account the joint spatio-temporal

The classical notions of structural controllability and structural observability are receiving increasing attention in Network Science, as they provide a mathematical basis to understand how the network structure of a dynamic system affects its controllability and observability properties. However, these two notions are formulated assuming systems with linear dynamics, which significantly limits their

The majority of research on epidemics relies on models which are formulated in continuous-time. However, processing real-world epidemic data and simulating epidemics is done digitally and the continuous-time epidemic models are usually approximated by discrete-time models. In general, there is no guarantee that properties of continuous-time epidemic models, such as the stability of equilibria, also

Even though the widespread use of social platforms provides convenience to our daily life, it causes some bad results at the same time. For example, misinformation and personal attack can be spread easily on social networks, which drives us to study how to block the spread of misinformation effectively. Unlike the classical rumor blocking problem, we study how to protect the targeted users from being

In this paper, we respectively discuss the finite-time consensus (FTC) and finite-time $H_{\infty }$ consensus (FTHC) problems for a kind of nonlinear multi-agent systems under directed topology. Firstly, with the help of some inequality techniques and the designed state feedback controller, a FTC criterion is presented for multi-agent system. In addition, two adaptive control strategies for ensuring

Fingerprint-based indoor localization methods are promising due to the high availability of deployed access points and compatibility with commercial off-the-shelf user devices. However, to train regression models for localization, an extensive site survey is required, which collects fingerprint data from the target areas. In this paper, we consider the problem of informative path planning (IPP) to

Public health services are constantly searching for ways to reduce the spread of infectious diseases, such as public vaccination of asymptomatic individuals, quarantine and treatment of symptomatic individuals. In this paper, we introduce epidemic models including variable population size, degree-related imperfect vaccination and quarantine on scale-free networks. More specifically, the models are

The transfer learning methodology leverages knowledge from the source domain with abundant training data to the insufficient target domain. Recently, new approaches continue to be developed and used to solve different classification tasks, ranging from public news to videos and to many others. Most transfer learning methods are based on the assumption that both source and target data are in the same

Deploying drone mounted base stations (DBSs) can quickly recover communications of mobile users (MUs) in a disaster struck area. That is, DBSs can act as relay nodes to transmit data from remote working base stations (which are located out of the disaster struck area) to MUs. Since DBSs could be deployed very close to MUs, the access link data rates between DBSs and MUs are well provisioned. However

The finite-time stability (FTS) is crucial for the network systems with high requirements on the convergence rate. When the finite-time settling time is globally bounded, the fixed-time stability (FxTS) is introduced. This paper focuses on the FTS/FxTS of the network systems with nonlinear protocols over signed digraphs. Sufficient criteria are obtained for ensuring the FTS/FxTS of the investigated

Water distribution networks are critical infrastructures under threat from the accidental or intentional release of contaminants. Large-scale data collection is vital for digital twin modelling, but remains challenging in underground spaces over vast areas. Therefore, inferring the contaminant spread process with minimal sensor data is important. Existing sensor deployment optimisation approaches use

In random graph models, the degree distribution of an individual node should be distinguished from the (empirical) degree distribution of the graph that records the fractions of nodes with given degree. We introduce a general framework to explore when these two degree distributions coincide asymptotically in a sequence of homogeneous random networks of increasingly large size. The discussion is carried

Many real-world networks are comprised of several networks interconnected with each other and they may have different topologies and epidemic dynamics. The dynamical behaviors of various epidemic models on coupled networks have attracted great attentions. For instance, many people adhere to the statutory working and rest days, which results in a periodic fluctuation of disease transmission in the population

In this paper we consider the problem of controlling a limited number of target nodes of a network. Equivalently, we can see this problem as controlling the target variables of a structured system, where the state variables of the system are associated to the nodes of the network. We deal with this problem from a different point of view as compared to most recent literature. Indeed, instead of considering

Identifying community structure of a complex network provides insight to the interdependence between the network topology and emergent collective behaviors of networks, while detecting such invariant communities in a time-varying network is more challenging. In this paper, we define the temporal stable community and use the temporal similarity of edges for judging whether temporal stable communities

In this big data era, more and more social activities are digitized thereby becoming traceable, and thus the studies of social networks attract increasing attention from academia. It is widely believed that social networks play important role in the process of information diffusion. However, the opposite question, i.e., how does information diffusion process rebuild social networks, has been largely

The advancement of networking platforms for assured online services requires robust and effective network intelligence systems against anomalous events and malicious threats. With the rapid development of modern communication technologies, artificial intelligence, and the revolution of computing devices, cloud computing empowered network intelligence will inevitably become a core platform for various

Several applications in a large scale network depend on diffusion of information from one node to others. In this paper, we propose a diffusion protocol for networked multi-agent systems based on both the structure of the network and the priority of agents. The agents (nodes in the network) interact with their neighbors for the diffusion of information based on the weighted difference of the resources