IEEE Transactions on Network Science and Engineering

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From the April-June 2017 issue

Competitive Propagation: Models, Asymptotic Behavior and Quality-Seeding Games

By Wenjun Mei and Francesco Bullo

Featured articleIn this paper we propose a class of propagation models for multiple competing products over a social network. We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors. A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products. According to the chronological order of social and self conversions, we develop two Markov-chain models and, based on the independence approximation, we approximate them with two corresponding difference equations systems. Our theoretical analysis on these two approximated models reveals the dependency of their asymptotic behavior on the structures of both the product-conversion graph and the social network, as well as the initial condition. In addition to the theoretical work, we investigate via numerical analysis the accuracy of the independence approximation and the asymptotic behavior of the Markov-chain model, for the case where social conversion occurs before self conversion. Finally, we propose two classes of games based on the competitive propagation model: the one-shot game and the dynamic infinite-horizon game. We characterize the quality-seeding trade-off for the first game and the Nash equilibrium in both games.

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  • We are pleased to announce that TNSE is now indexed in the Clarivate Analytics Emerging Sources Citation Index (ESCI), a new edition of the Web of Science.

  • 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 Scalability and Privacy in Social Networks

Submission deadline: September 1, 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: October 1, 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.


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TNSE is financially cosponsored by:

IEEE Computer SocietyIEEE Circuits and Systems Society IEEE Comunications Society

 

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IEEE Control Systems SocietyIEEE Signal Processing Society