Piscataway, NJ (PRWEB) March 25, 2015
Digital technology is changing our social networks, as well as the tools we have for studying and designing those networks. In the future, those changes won’t just impact the careers of engineers, mathematicians and computer scientists—they could change the structure of society itself. In a recent issue of Proceedings of the IEEE, the most highly cited general interest journal in electrical engineering and computer science, is dedicated to exploring those and other topics.
In the United States, 72 percent of Internet users have adopted social media. Just 10 years ago, Facebook had 1 million users, and today it’s up to 1.15 billion. In the three years since Google+ arose, 1 billion users have created accounts. On average, Internet users in the U.S. spend nearly 15 minutes of every hour on social media, and 50 percent of Americans report that Facebook is their No. 1 influencer when it comes to purchases. We have nothing in history to compare with that kind of rapid growth and the changes it has meant for the size of social networks, the spreading of power and influence, the globalization of information and the rapid ability to make changes in institutions.
As the digital world and physical world become increasingly intertwined, the ramifications are far-reaching. Social media, search and data extraction technologies are not only altering the structure and dynamics of social networks, but are also potentially changing how controllable these systems are. This special issue reviews what is currently known about how these technologies are changing social networks and what the consequences will be for human social dynamics.
The papers in this special issue of Proceedings of the IEEE include:
“Tracking the Digital Footprints of Personality”: More and more of our interactions, both online and off, are creating digital footprints. Because of that, big social data offers unprecedented insights into population-wide patterns, as well as detailed characteristics of individuals. This paper explores how our digital footprints—including Facebook profiles and mobile devices—can help infer a person’s psychological profile. It introduces a range of works focusing on predicting personality and concludes with a discussion about the implications this bears in terms of privacy, data ownership and opportunities for research in computational science in the future.
“Impact of Changing Technology on the Evolution of Complex Informational Networks”: We are increasingly connected by technology, and that is changing the structure of how we interact among ourselves and with computational devices. Complex networks act as mathematical graphs connecting nodes (people and computers) via edges (relationships, wires). While much research has been carried out, there remains much to explore in the fundamental principles that drive network growth in human societies and in worldwide computer networks: specifically, the formal connection between large empirical studies of network evolution and fundamental concepts of information, learning and social theory. This paper formalizes the general problem of learning and computation in network environments in terms of average structural network changes, and proposes a conceptual framework to explain the transition from initially static, undifferentiated, and information-poor environments to dynamic, richly diverse, and interconnected systems. It also sheds light on expected changes to urban form and function to computational hardware.
A sampling of other papers within the issue include “Analyzing Temporal Networks in Social Media;” “Words on the Web: Noninvasive Detection of Emotional Contagion in Online Social Networks;” “Social Persuasion in Online and Physical Networks;” “Overlapping Communities Explain Core–Periphery Organization of Networks,” and more.
Guest editors for this issue include Jessica C. Flack, who is co-director of the Center for Complexity and Collective Computation (C4) at Wisconsin Institute for Discovery, University of Wisconsin in Madison, Wisconsin, and external professor at the Santa Fe Institute, Santa Fe, New Mexico. Her research focuses on coarse graining and collective computation in nature and their role in the evolution and development of new levels of biological and social organization. Raissa M. D’Souza is a professor of computer science and of mechanical engineering at the University of California, Davis, in Davis, California, as well as an external professor at the Santa Fe Institute in Santa Fe, New Mexico. Her interdisciplinary work on network theory spans the fields of statistical physics, theoretical computer science and applied math.
To learn about all of these concepts, and more, visit the Proceedings of the IEEE website.