"Structure and Dynamics of Social Networks and Other Networked Systems."

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The principal investigator and his colleagues study thestructure and function of real-world networked systems,particularly information, computer, and social networks. Thework includes an empirical component concerned with the discoveryand analysis of network structure, including collaborationnetworks, citation networks of papers and of registered patents,transportation and other geographic networks, social networks,and computer networks of various kinds including administratorand server-client networks and networks of "weblogs." Propertiesstudied include correlation functions, spatial structure,centrality and similarity measures, and measures of assortativemixing and community structure. There is also a theoreticalcomponent to the work, involving the development of models to aidin the understanding of the effects of network structure. Ofparticular interest are generalized random graph models ofnetworks, percolation models of network resilience, growthmodels, exponential random graphs, and Markov graphs. Theproject also includes the development of new computer algorithmsfor extracting and visualizing network structure, particularlyfor the detection of community structure in networks. The investigator and colleagues try to understand the formand behavior of networks, particularly information networks,computer networks, and social networks. The work has bearing on anumber of issues of current importance. A knowledge of thestructure of networks of acquaintance is crucial to anunderstanding of how information, such as news, rumors, consumertrends, etc., spreads through society. Similarly, networks ofphysical contact between people govern the way in which diseasesspread and computer networks govern the spread of computerviruses. A proper understanding of the nature and progress ofinfections is impossible without good network models. The workdescribed here is aimed both at determining what the structure ofthe networks in question is, and also at modeling the effect ofthat structure on possible outcomes. As well as enhancing basicunderstanding of these problems, the results point to ways inwhich network structure or dynamics can be changed in order toeither improve network transmission (in the case of information)or slow it down (in the case of epidemics). For diseasetransmission, for instance, it may be possible to suggesteffective targets for immunization or education campaigns to slowdisease spread. New data resources and analysis techniques alsoare developed that will aid future work by other researchers onrelated topics.