The observation of patterns that characterize networks, from biological to technological and social, and the impact of the Web and the Internet on society and business have motivated interdisciplinary research to advance our understanding of these systems. Their study has been the subject of Network Science research for a number of years. However, more recently we have witnessed the emergence of two new interdisciplinary areas: Web Science and Internet Science.
Network Science can be traced to its mathematical origins dating back to Leonard Euler's seminal work on graph theory15 in the 18th century and to its social scientific origins two centuries later by the psychiatrist Jacob Moreno's25 efforts to develop "sociometry." Soon thereafter, the mathematical framework offered by graph theory was also picked up by psychologists,2 anthropologists,23 and other social scientists to create an interdiscipline called Social Networks. The interdiscipline of Social Networks expanded even further toward the end of the 20th century with an explosion of interest in exploring networks in biological, physical, and technological systems. The term Network Science emerged as an interdisciplinary area that draws on disciplines such as physics, mathematics, computer science, biology, economics, and sociology to encompass networks that were not necessarily social.1,26,35 The study of networks involves developing explanatory models to understand the emergence of networks, building predictive models to anticipate the evolution of networks, and constructing prescriptive models to optimize the outcomes of networks. One of the main tenets of Network Science is to identify common underpinning principles and laws that apply across very different networks and explore why in some cases those patterns vary. The Internet and the Web, given their spectacular growth and impact, are networks that have captured the imagination of many network scientists.13 In addition, the emergence of online social networks and the potential to study online interactions on a massive, global scale hold the promise of further, potentially invaluable insights to network scientists on network evolution.24
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