The last two decades have been a particularly productive period for network analysis in the social sciences. While the roots of formal graph modeling of social behavior go back a half a century or more, most of the early work was devoted to developing formalisms and tool building. That started to change a generation ago as new cohorts of PhDs began applying network analysis to a much wider variety of practical problems in the disciplines. In sociology, for example, in relatively short order during the 1990s, the sociology of social movements, the sociology of markets, even the sociology of culture was significantly transformed by innovations coming from Network Science. In communication, studies of inter-organizational collaboration, team processes, and organizational change took a decidedly network turn. Now the rise of the Internet and the increasing availability of Big Data promise to transform the scientific study of social networks yet again. For example, not that long ago it was inconceivable that a 29 minute video about a Ugandan warlord, Kony, posted on You Tube, produced by a very small non-governmental organization, Invisible Children, would go so viral. In just four days in March 2012, it was viewed by over 60 million people throughout the world, mentioned tens of thousands of times on Twitter, featured in thousands of media outlets, and debated by hundreds of organizational bloggers. The social influence processes that underlie such contagion and subsequent delegitimation of claims and calls to collective action have been previously theorized, but the data to test the theories have heretofore been unaccessible to social scientists.

Perhaps the greatest barrier to advancing social scientific knowledge has been the sheer difficulty of gathering social data (which has largely been done by hand-coding). Now, with the availability of Big Data, large swaths of human interaction, both those domains that are directly mediated by computer networks (such as tweets and blogs) as well as non-digital interactions and events that may be later recorded or described on the Internet (think of postings on Facebook) can be tapped to provide a virtual fire-hose of real time social data. Moreover, the interactional systems that are captured by Big Data afford an opportunity to study social networks at a scale and complexity that is unprecedented. For their part, social scientists bring a rich collection of theories, insights and hypotheses about the role of networks in a wide variety of social settings and institutional spheres. Both the interactional networks and the communicative content that they carry are subject to network analysis. Key questions have to do with how to usefully blend the insights of social science with the power to gather and analyze Internet data streams of Big Data.

Related Training Modules

• M6: Modeling Network Evolution in Metric Spaces
• M9: Determining Polarizing Entities and Opinions in Online Networks
• M10: Network Science of Teams
• M13: Modeling Rhetoric
• M14: An Empirical Analysis of Sexual Networks and Pregnancy in Ghana
• M15: Narrative (Counter-)Mobilizations in U.S. Same-Sex Marriage Struggle
• M16: Learning Path Generation
• M17: Models of Social Power Evolution
• M19: A Privacy Model for Life Cycle Inventory Databases
• M20: Sociological Approach to Learning Path Generation
• M21: Epidemic Propagation Over Contact Networks
• M22: Information Networks in the Moral Narrative Analyzer (MoNA)
• M26: Embedding a Network into Latent Space
• M27: Understanding and Modeling Complex Network Processes
• M29: FLoRa Framework
• M31: Development of a Reader Network
• M41: Heterosexual network structure and experiences of sexual discrimination ...
• M43: Semantic Construction Through Twitter in the Colombian Peace Process
• M47: Visualizing Unknown Variables at Varying Scales in a GIS
• M48: Mapping slums using machine learning, remote sensing, and volunteered geographic information