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

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

The development of social networks and ubiquitous sensing promotes the network space into a new stage, which integrates the cyber network, physical network, and social network into cyber–physical–social networks (CPSN). In this paper, we propose a CPSN-based service framework. The framework firstly represents CPSN as an adjacency tensor. Then, a novel tensor decomposition method named high-order orthogonal

In the standard mechanism design framework, agents’ messages are gathered at a central point and allocation/tax functions are calculated in a centralized manner, i.e., as functions of all network agents’ messages. This requirement may cause communication and computation overhead and necessitates the design of mechanisms that alleviate this bottleneck. We consider a scenario where message transmission

As demand for electric vehicles (EVs) is expanding, meeting the need for charging infrastructure, especially in urban areas, has become a critical issue. One method of adding charging stations is to install them at parking spots. This increases the value of these spots to EV drivers needing to charge their vehicles. However, there is a cost to constructing these spots and such spots may preclude drivers

Influence maximization is an important technique in advertisement post, viral marketing, and public opinion monitoring. Seed set identification is one of the key issues in influence maximization. In reality, there exist heterogeneous nodes, such as user nodes, message nodes in social networks. The complex association relationship among heterogeneous nodes, which are seldom considered, significantly

Internet of Things (IoT) is considered as the future network to support machine-to-machine communications. To realize IoT network, a large number of IoT devices need to be deployed, which will lead a problem of allocating sufficient spectrum for the rapidly increasing devices. Through cooperative spectrum sharing, the unlicensed IoT devises (UIDs) help forward the signal of primary users (PUs) to exchange

With the development of GPS technology, GPS-equipped taxicabs and users with GPS-enabled devices interact each other, and form a new mobile Internet of things (M-IoT). This paper proposes a M-IoT service framework to predict multiusers’ mobility pattern by solving a cubical user-spatio-temporal probability map arising from heterogeneous sensor data. Then, the framework provides services for the traffic

User provided network (UPN) allows a user with high channel quality to share the network access for users with poor channel quality. As a result, both the quality of experience and efficiency of network resource can be improved by UPN. However, the success of UPN relies heavily on the willingness of users to participate in sharing, so the design of incentive mechanisms is critical for UPN. In this

Network systems, such as Internet, smart grids, transportation networks, social networks, etc., play a critical role in human society. However, due to their inherent vulnerability as well as the limited management and operational capability, these network systems are constantly under the threat of malicious attackers. Therefore, in such attack-defense scenarios, it is particularly significant to make

Online social networks (OSNs) often refuse to publish their social network graphs due to privacy concerns. Recently, differential privacy has become the widely accepted criteria for privacy preserving data publishing. Although some work has been done on publishing matrices with differential privacy, they are computationally unpractical as they are not designed to handle large matrices such as adjacency

It has recently been shown that the average energy required to control a subset of target nodes in a complex network scales exponentially with the cardinality of the subset. While the mean scales exponentially, the variance of the control energy over different subsets of target nodes can be large and has, as of yet, not been explained. Here, we provide an explanation of the large variance as a result

This paper provides a model to investigate information spread over cyber-social network of agents. The cyber-social network considered here comprises individuals and information sources. Each individual holds an opinion represented by a scalar that evolves over time. The information sources are stubborn, in the sense that their opinions are time-invariant. Individuals receive the opinions of information

We analyze optimal strategies for the allocation of a finite budget that can be invested in different advertising channels over time with the objective of influencing social opinions in a network of individuals. In our analysis, we consider both exogenous influence mechanisms, such as advertising campaigns, as well as endogenous mechanisms of social influence, such as word-of-mouth and peer-pressure

Modern cyber-physical systems are becoming increasingly interdependent. Such interdependencies create new vulnerabilities and make these systems more susceptible to failures. In particular, failures can easily spread across these systems, possibly causing cascade effects with a devastating impact on their functionalities. In this paper, we focus on the interdependence between the power grid and the

Cyber-physical social system (CPSS) has emerged to integrate the interaction between the physical, cyber, and social world. However, due to the ever-increasing amount of sensing data and the limited resources of mobile systems, how to allocate the tasks by crowd sensing to enable a high-confidence CPSS becomes a new challenge. Therefore, in this paper we propose a novel game theoretical approach to

The concept of social networking is integrated into the Cyber-Physical Systems (CPSs) for the purpose of allowing smart objects to establish social relationships in an autonomous way. Solutions based on social relationships are considered to be promising approaches for resource/service discovery. The primary issues in a social-CPS (SCPS) involve how to define, quantify and eventually establish social

Social networks are complex in their forming and growing processes. Tremendous empirical evidence in undirected social networks, such as Facebook, Quora and Foursquare, demonstrates that, to a large extent, individuals are associated with each other not by preference but through other organizing rules. One such rule found in many real social networks is the Henneberg growth mechanism, with which a

With many countries trying to establish their own smart grids, smart meters are massively deployed throughout the world. Although smart meters are manufactured with low tamper-resistant components, malicious users with just a moderate level of computer knowledge are able to launch cyber attacks. By manipulating electricity consumption readings to smaller values, malicious users can steal electricity

Cyber-Physical Systems (smart grid, smart transportation, smart cities, etc.), driven by advances in Internet of Things (IoT) technologies, will provide the infrastructure and integration of smart applications to accelerate the generation and collection of big data to an unprecedented scale. As a fundamental commodity in our current information age, big data is a crucial key to competitiveness in modern

To provide fine-grained access to different dimensions of the physical world, the data uploading in smart cyber-physical systems suffers novel challenges on both energy conservation and privacy preservation. It is always critical for participants to consume as little energy as possible for data uploading. However, simply pursuing energy efficiency may lead to extreme disclosure of private information

Due to the tremendous growth of online social networks in both participants and collected contents, social data publication has provided an opportunity for numerous services. However, neglectfully publishing all the contents leads to severe disclosure of sensitive information due to diverse user behaviors. Therefore, there should be a thoroughly designed framework for data publication in online social

In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks. This article proposes a new framework for inference attacks in social networks, which smartly integrates

Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applications. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant selection problems solved with auction-based approaches usually involve

Social networks allow rapid spread of ideas and innovations while negative information can also propagate widely. When a user receives two opposing opinions, they tend to believe the one arrives first. Therefore, once misinformation or rumor is detected, one containment method is to introduce a positive cascade competing against the rumor. Given a budget $k$ , the rumor blocking problem asks for $k$