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. To submit your manuscript, please use the ScholarOne Manuscripts manuscript submission site. 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
Social 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.
Editorials and Announcements
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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.
- Message from the Incoming Editor-in-Chief (April-June 2017)
- State of the Journal Editorial (April-June 2017)
- EIC Editorial (Jan-March 2016)
- IEEE Transactions on Network Science and Engineering (Jan-June 2014)
- IEEE TNSE Inaugural Issue Editorial (Jan-June 2014)
Call for Papers
Special Issue on Scalability and Privacy in Social Networks
Submission deadline: 1 Sept. 2017. View PDF.
The growing popularity of Online Social Networks and their emerging applications attracted much attention from both academia and industry. Due to their nature, social networks are considered as sources of Big Data containing large amounts of privacy-sensitive information. A social network is frequently abstracted using a mathematical model such as a graph, which is usually very large, that can later be used as an input to other algorithms for further processing. Recent reports show that if the abstractions of social networks are not properly designed, a large amount of private information can be extracted from them. As the area of Data Science and related technologies are getting more mature, it is highly possible that what is considered a safe abstraction of social networks today, becomes unsafe tomorrow. Unfortunately, the problem of designing privacy-aware social network abstractions is very challenging. Generally speaking, this is because a change in input data forces a change in the structure of the algorithms which will process the input data. Such change can also affect the output of the algorithm. Certainly, the emerging Big Data analytic techniques, such as differential analysis, will bring more complexity to this already-conundrum-like problem. Most importantly, any solution to this problem has to be scalable. This special issue aims to provide a prime venue for researchers from both academia and industry to discuss about this impelling, but not well-understood, problem.
Special Issue on Learning-based Modeling, Management and Control for Computer and Communication Networks
Submission deadline: 1 Oct. 2017. View PDF.
Computer and communication networks are becoming larger and more complicated, generating a huge amount of runtime statistics data (such as traffic load, resource usages, etc.) every second. Instead of treating big data in these networks as an unwanted burden, we aim to leverage them as a great opportunity for better understanding user demands and system capabilities such that we can optimize network operations to better serve users and applications. Emerging machine learning models and techniques, such as active learning, Deep Neural Networks (DNNs), Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL), have been shown to dramatically improve the state-of-the-art of many applications, including video/image processing, natural language processing, game playing, etc. However, research on learning-based modeling, management and control in computer and communication networks is quite scattered. This special issue aims to exploit how these emerging and powerful techniques (including active learning, Deep Learning (DL), DRL, etc.) can be leveraged to grasp the exciting opportunities provided by pervasive availability of voluminous data to model, manage and control computer and communication networks.
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.
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