Comparative Network Analysis: Mapping Global Social Interactions

点击翻译按钮获取中文摘要

Creating Tools to Map Global Network Structures The project aims to advance methodological, descriptive, and theoretical knowledge of variations inhuman 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 projectwill 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 ofsocial interaction across countries with diverse cultures, political systems, mobility patterns, andlevels 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-nationalmeasures 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 networkscientists 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 marketresearch on the diffusion of innovations, 4) for social movement research on "domino effects" like thoseobserved 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 benefitfrom comparative data that may be helpful for tailoring the design of online social networksites. Network scientists will have access to a rich new source of social network data on a global scale. Finally, thedevelopment and dissemination of effective methods to collect, manage, analyze, and comparemultiple networks of hundreds of millions of nodes will constitute a significant contribution to thesocial science community, especially when coupled with technical education and training of socialscience undergraduate and graduate students to effectively utilize research tools anddistributed/parallel computing frameworks for analyzing very large datasets.