IEEE Transactions on Network Science and Engineering

IEEE Transactions on Network Science and Engineering (TNSE) publishes peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. Read the full scope of TNSE.

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From the July-September 2017 issue

Inference of Hidden Social Power Through Opinion Formation in Complex Networks

By Omid Askari Sichani and Mahdi Jalili

Featured articleSocial networks analysis and mining gets ever-increasing importance in various disciplines. In this context finding the most influential nodes with the highest social power is important in many applications including spreading of innovation, opinion formation, immunization, information propagation and recommendation. In this manuscript, we propose a mathematical framework in order to effectively estimate the social power (influence) of nodes from time series of their interactions. We assume that there is a connection network on which the nodes interact and exchange their opinions. The time series of the opinion values (with hidden social power values) are taken as input to the proposed formalism and an optimization approach results the estimated for the social power values. We propose an estimation framework based on Maximum-a-Posteriori method that can be converted to a convex optimization problem using Jensen inequality. We apply the proposed method on a number of model networks and show that it correctly estimates the true values of the social power. The proposed method is not sensitive to the specific form of social power used to produce the time series of the opinion values. We also consider an application of finding influential nodes in opinion formation through informed agents. In this application, the problem is to find a number of influential nodes to which the informed agents should be connected to maximize their influence. Our numerical simulations show that the proposed method outperforms classical heuristic methods including connecting the informed agents to nodes with the highest degree, betweenness, closeness, PageRank centralities or based on a state-of-the-art opinion-based model.

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Editorials and Announcements


  • TNSE is pleased to participate in a free trial offering of the new IEEE DataPort data repository, which supports authors in hosting and referring to their datasets during the article submission process. Learn more about this exciting opportunity.
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  • We are pleased to announce that Dapeng Oliver Wu, a professor in the Department of Electrical & Computer Engineering at the University of Florida, has been named the new 2017-2018 EIC for the IEEE Transactions on Network Science and Engineering.


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Call for Papers

Special Issue on Network Science in Biological and Bio-inspired Systems

Submission deadline: 31 Dec. 2017. View PDF.

Network science offers a novel framework for systematically exploring the structure and functions of biological and bio-inspired systems, which has attracted much attention within the IEEE and beyond. Indeed, networks have been widely used, not only to represent the couplings of various types, e.g., synapses that connect neurons (connectome), physical contacts between proteins (interactome), chemical reactions of metabolism, gene regulation, and swarming interactions, but also to analyze the functions and underlying working mechanisms of these systems. Given the growing activity of both theory and applications across computer science, physics and biology, the aim of this special issue is to provide a venue for researchers with different backgrounds to discuss the fruitful results, recent advances and challenges in the interdisciplinary area of biological and bio-inspired networks.

Special Issue on Big Data and Artificial Intelligence for Network Technologies

Submission deadline: 30 Jan. 2018. View PDF.

Generation of huge amounts of data, called big data, across different sectors such as banking, healthcare, retail and education, among others, is creating the needs for efficient tools to manage those data. Artificial intelligence (AI) has become the powerful tools in dealing with big data with recent breakthroughs at multiple fronts in machine learning, including deep learning. Meanwhile, information networks are becoming larger and more complicated, generating a huge amount of runtime statistics data such as traffic load, resource usages. The emerging big data and AI technologies may include a bunch of new requirements, applications and scenarios such as ehealth, Intelligent Transportation Systems (ITS), Industrial Internet of Things (IIoT), and smart cities in the term of computing networks. The big data and AI driven network technologies also provide an unprecedented patients to discover new features, to characterize user demands and system capabilities in network resource assignment, security and privacy, system architecture, modeling and applications, which needs more explorations. We believe that these explorations will greatly benefit the academia and Information and Communication Technologies (ICT) industries.

Special Issue on Network of Cyber-Social Networks: Modeling, Analyses, and Control

Submission deadline: 1 Feb. 2018. View PDF.

Network of cyber-social networks (NCN) is a promising new area that has recently attracted significant interests. Its core differentiator is the tight conjoining among heterogeneous cybersocial networks or between cyber networks and physical networks. The focus of this proposed special issue is to address the interdependence of NCN components that facilitates its modeling, analysis, and control.

Over the past decade, we have witnessed an unprecedented growth of communication networks and online social networks in cyber world that “teleports” us to interact with remote people and/or objects. With the development of cyber technologies, heterogeneous networks become increasingly interdependent. Examples include mobile social networks that exploit social ties, modern power networks that rely on the control and communication over the Internet, and intelligent transportation networks that utilize communication networks to monitor traffic congestion in real time. A mobile social network is a typical multi-layer NCN where the shortrange device-to-device communication is influenced by the human social behavior (trust and reciprocity). A modern power network demonstrates a fundamental NCN property where the interconnecting links between networks may induce or impede the risk of cascading failures. Until recently, these heterogeneous networks were mostly treated as separate entities, leaving their complex interactions unexplored.

Special Issue on Network Science for High-Confidence Cyber-Physical Systems

Submission deadline: 1 Mar. 2018. View PDF.

Cyber-Physical Systems (CPS) refer to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. Today’s CPS involve smart devices that bring into CPS ubiquitous intelligence, leading to the innovation and competition within sectors such as agriculture, healthcare, transportation, energy, manufacturing, environment, building design and automation, etc., which can completely transform the ways people interact with the physical world. To enable high-confidence CPS for better benefits realization as well as supporting emerging applications, network science based theories and methodologies are needed to cope with the ever-growing complexity of smart CPS, to predict the system behaviors, and to model the deep inter-dependencies among CPS and the natural world. Nevertheless, current research on network science approaches to investigate the challenges in high-confidence CPS is quite scattered. The major objective of this special issue is to exploit various network science techniques such as modeling, analysis, mining, visualization, and optimization to advance the science of supporting high-confidence CPS for greater assurances of security, safety, scalability, efficiency, and reliability.

Special Issue on Network Science for Internet of Things (IoT)

Submission deadline: 1 Apr. 2018. View PDF.

Internet of Things (IoT) applications have been growing significantly in recent years and the so-called IoT ecosystem enables seamless connectivity that is enabling many applications such as smart home, smart health, connected vehicle and smart grid and others. The network infrastructure, connectivity and dynamics in the IoT ecosystem are becoming increasingly complex, scalable and heterogeneous, opening up many challenges for network sciences and system engineering including architectural, operational, service and security challenges. In addition, the interconnection and cooperation of things (e.g., connected sensors and devices) rely on the both the network environment, service provided and the physical environments. However, many related topics such as network control, evolutions, efficiency, security, privacy, network properties, network and device heterogeneity have not been well studied in the literature for the IoT ecosystem. There is a strong need to understand the fundamental characteristics such as control structure, functions and behavior of networks both from a theoretical and practical perspective for future IoT. In addition, since IoT applications generate huge amounts of network traffic over networks, network issues such complexity, efficiency, dynamics, interferences and interaction and robustness need to be reviewed on a large scale. We aim to leverage them in order to better understand network performance bound, user demands and experience and capacity of the IoT network infrastructure which will enable the network to seamlessly connect to IoT devices and support the emerging applications of IoT users. In summary, research on network sciences and engineering for inter-disciplinary IoT applications is still in its infancy. For example, more research efforts are needed to examine the service quality of connected health applications from a network science perspective and principle. This special issue will present how recent and future advances in network science and engineering can be leveraged to enable future and emerging IoT applications.

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