The principal investigator and his colleagues study the structure and function of real-world networked systems, particularly information, computer, and social networks. The work includes an empirical component concerned with the discovery and analysis of network structure, including collaboration networks, citation networks of papers and of registered patents, transportation and other geographic networks, social networks, and computer networks of various kinds including administrator and server-client networks and networks of "weblogs." Properties studied include correlation functions, spatial structure, centrality and similarity measures, and measures of assortative mixing and community structure. There is also a theoretical component to the work, involving the development of models to aid in the understanding of the effects of network structure. Of particular interest are generalized random graph models of networks, percolation models of network resilience, growth models, exponential random graphs, and Markov graphs. The project also includes the development of new computer algorithms for extracting and visualizing network structure, particularly for the detection of community structure in networks.
The investigator and colleagues try to understand the form and behavior of networks, particularly information networks, computer networks, and social networks. The work has bearing on a number of issues of current importance. A knowledge of the structure of networks of acquaintance is crucial to an understanding of how information, such as news, rumors, consumer trends, etc., spreads through society. Similarly, networks of physical contact between people govern the way in which diseases spread and computer networks govern the spread of computer viruses. A proper understanding of the nature and progress of infections is impossible without good network models. The work described here is aimed both at determining what the structure of the networks in question is, and also at modeling the effect of that structure on possible outcomes. As well as enhancing basic understanding of these problems, the results point to ways in which network structure or dynamics can be changed in order to either improve network transmission (in the case of information) or slow it down (in the case of epidemics). For disease transmission, for instance, it may be possible to suggest effective targets for immunization or education campaigns to slow disease spread. New data resources and analysis techniques also are developed that will aid future work by other researchers on related topics.
