EITM: The Structure, Evolution, and Function of Large-Scale Social Networks: Theory, Data, and Experiment.

Columbia University, New York NY

Investigators

Abstract

Social scientists have long recognized that networks are central to a wide variety of social processes, for example, carrying information, transmitting disease, exerting influence, and providing access to resources. They have, however, been hampered in their study of networks by limited availability of large data sets and the intractability of complex network models. Consequently, network analysts have historically-focused their analytical attention on small-scale networks, and relied on their resulting intuitions to understand larger-scale processes. In the past five years, physicists, biologists, mathematicians, and computer scientists have brought to the study of very large-scale networks valuable computational and analytical tools in their attempts to understand the behavior of networked dynamical systems. This study will advance network analysis by integrating theory, simulation, data, and experiment. In contrast to traditional methods, where links are self-reported or inferred from affiliation data, the study will examine networks that are generated by recorded information exchange between social actors with specific demographic and activity- based characteristics. Specifically, the PI will complete the following distinct, but highly complementary, projects: (1) develop a parsimonious yet sociologically plausible, mathematical model of network evolution; (2) analyze data drawn from a real-time observational study of a university e-mail network; and (3) complete a large-scale Internet experiment that explores global network structure and utility through the lens of social search. In addition, to its intellectual contribution both to the field of social network analysis and also to the network research literature that now extends well beyond sociology, the project will contribute to the development of more realistic social network models, along with a better empirical picture of the relevant phenomena. These have the potential to contribute to predictions and policy recommendations pertaining to a range of social problems such as the spread of cultural influence and infectious disease, social mobilization, and the distribution and utilization of social capital. Finally, the project will train graduate and undergraduate students at the cutting edge of mathematical sociology and network-related research and will make available online both empirical data sets and simulation/analysis software. This has the potential to generate significant spillover benefits to the broader scientific community, especially those researchers interested in further developing the field of network analysis.