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 January-March 2018 issue

A Micro-Foundation of Social Capital in Evolving Social Networks

By Ahmed M. Alaa, Kartik Ahuja, and Mihaela van der Schaar

Featured article A social network confers benefits and advantages on individuals (and on groups); the literature refers to these benefits and advantages as social capital. An individual’s social capital depends on its position in the network and on the shape of the network—but positions in the network and the shape of the network are determined endogenously and change as the network forms and evolves. This paper presents a micro-founded mathematical model of the evolution of a social network and of the social capital of individuals within the network. The evolution of the network and of social capital are driven by exogenous and endogenous processes—entry, meeting, linking—that have both random and deterministic components. These processes are influenced by the extent to which individuals are homophilic (prefer others of their own type), structurally opportunistic (prefer neighbors of neighbors to strangers), socially gregarious (desire more or fewer connections) and by the distribution of types in the society. In the analysis, we identify different kinds of social capital: bonding capital refers to links to others; popularity capital refers to links from others; bridging capital refers to connections between others. We show that each form of capital plays a different role and is affected differently by the characteristics of the society. Bonding capital is created by forming a circle of connections; homophily increases bonding capital because it makes this circle of connections more homogeneous. Popularity capital leads to preferential attachment : individuals who become popular tend to become more and more popular because others are more likely to link to them. Homophily creates inequality in the popularity capital attained by different social categories; more gregarious types of agents are more likely to become popular. However, in homophilic societies, individuals who belong to less gregarious, less opportunistic, or major types are likely to be more central in the network and thus acquire a bridging capital. And, while extreme homophily maximizes an individual’s bonding capital, it also creates structural holes in the network, which hinder the exchange of ideas and information across social categories. Such structural holes represent a potential source of bridging capital: non-homophilic (tolerant or open-minded) individuals can fill these holes and broker interactions at the interface between different social categories.

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Editorials and Announcements

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.
  • 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 Intelligent Network Management

Submission deadline: 30 May 2018. View PDF.

With the development of IT technology, communication networks have been evolving from a medium of data exchange to a platform providing diverse services. Recently, operators have started to explore how to use Artificial Intelligence (AI) to simplify, optimize and intelligently assist with network management and control to reduce operational costs and to improve performance and user experience. Recent breakthroughs from big data technology have accelerated this interest.

In general, such AI technologies will enable operators to gain a deeper understanding of the network dynamics and enable more accurate forecasts of network trends. While it is important to gain such knowledge, the more challenging question is how to apply such knowledge to improve the network performance, i.e., to improve planning and operating decisions for managing the network. This is not straightforward for many reasons. For example, current decision making models were not developed for using such knowledge, and efforts made to gain and apply such knowledge is at an early stage. Additionally, forecasts based on such knowledge have a shelf life, and may become invalid after network operating policies are changed. For example, users may adjust their demand patterns in response to network changes. Therefore, new methods are needed for implementing intelligent network management. A three stage closed loop model is needed to uncover useful patterns through: 1) closely observing the network, 2) forecasting network trends, 3) applying the knowledge gained to improve the network.

Addressing the above challenge involves knowledge and skills from different disciplines, spanning from AI, networking, optimization, game theory, and so on. This special issue (SI) calls for research on intelligent network management. While the scope of intelligence application can be broadly defined as network performance analysis and forecasting, we are especially interested in network operational functions such as capacity planning, resource provisioning, routing, faulty recovery, etc. We welcome theoretical work for building scientific foundations, as well as cases of specific applications to demonstrate the potential.

Special Issue on Economics of Modern Networks

Submission deadline: 1 Sept. 2018. View PDF.

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 design and operation issues in these modern networks. This special issue solicits the state-of-art economic modeling and analysis results on a wide range of modern networks, such as Internet, wireless networks, energy networks, transportation networks, social networks and supply chain networks. We are particularly interested in contributions that can address issues in more than one type of networks.


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TNSE is financially cosponsored by:

IEEE Computer SocietyIEEE Circuits and Systems Society IEEE Comunications Society

 

TNSE is technically cosponsored by:

IEEE Control Systems SocietyIEEE Signal Processing Society