Human Networks: Users, Content, and Social Organization

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

Affiliated Faculty

Databases, Distributed Systems, Cloud Computing, Social Networks

Analysis and modeling of dynamic processes in networks.

Computer networks, wireless networks, social networks, networking in developing regions

Control theory, Multi-agent networks, Robotic coordination, Power systems

Epidemiology & Mathematical modeling; social network analysis; infectious disease epidemiology

Databases, Distributed Systems, Cloud Computing, Social Networks

Collective action, Social media, Information and source credibility

Ecology, Evolution, and Marine Biology

Ecological cascades, community assembly and disassembly, defaunation, and ecosystem structure and function.

Spatial-temporal modeling of moral networks, media neuroscience, research methods


Technology and social change; intersectionality; sexuality, gender, race

Computer Science
NLP, Text Mining, Information Retrieval, Information and Code Theory, Linguistics, Graph Theory, Networks

Semantic Networks, Cultural Sociology, Diversity in Organizations

Multi-agent systems, Distributed sensing and decision making, Wireless comm.

Spatial optimization; geographic information science; urban, regional, and natural resource planning and development; spatial analytics; location modeling; transportation; spatial decision support system and GIS application and development

Computational science and engineering, Biological and ecological networks

Director of Network Science IGERT
Network analysis, Data mining, Bioinformatics

Social networks, Collective action and technology, Globalization processes

Computer Science

Human computer interaction, artificial intelligence, machine learning, and cognition, network science and big data

Media neuroscience, research methods and statistics