The project aims to advance methodological, descriptive, and theoretical knowledge of variations in human societies on a global scale. Methodologically, the project will develop accessible data collection, management, and analysis tools for mapping network topology at the national level. In addition, the project will develop statistically valid and efficient methods for comparing social networks that differ in size and density. Descriptively, as the first comparative mapping of global social networks, the project will provide a more detailed and comprehensive picture of the similarities and differences in patterns of social interaction across countries with diverse cultures, political systems, mobility patterns, and levels of economic development. Theoretically, the project will address fundamental questions about social and structural differences between countries with different network topologies. The project will draw upon widely used cross-national measures of individualism and collectivism to test hypothesized structural differences between the US, UK, China and Korea. The findings will catalyze theoretical and methodological dialog between network scientists in disciplines that emphasize universals (e.g. statistical mechanics)and variations (e.g. cross-cultural studies in sociology and anthropology).
Broader Impacts This project will have broad applications: 1)for the spread of pathogens and public health countermeasures, 2) for policy research on the economic and cultural correlates of the "digital divide",3) for market research on the diffusion of innovations, 4) for social movement research on "domino effects" like those observed in the cascading collapse of the former Soviet Union and more recently in the Arab Spring, and 5) comparative studies of social capital and economic development. Computer scientists will benefit from comparative data that may be helpful for tailoring the design of online social network sites. Network scientists will have access to a rich new source of social network data on a global scale. Finally, the development and dissemination of effective methods to collect, manage, analyze, and compare multiple networks of hundreds of millions of nodes will constitute a significant contribution to the social science community, especially when coupled with technical education and training of social science undergraduate and graduate students to effectively utilize research tools and distributed/parallel computing frameworks for analyzing very large datasets.
