The principal investigator and his colleagues study the structure and function of real-world networked systems, particularly but not exclusively social networks. An empirical component is concerned with the discovery and analysis of the structure of networks, including networks of collaboration between scientists, networks of company directors, networks of personal preferences, and networks of citations between academic publications. Studied quantities include local observables such as transitivity and degree distribution, and nonlocal ones such as centrality and community structure. The investigator develops models to aid in the understanding of the effects of network structure. Of particular interest are random graph models of networks, percolation models of network resilience, and models of epidemics taking place on social networks. The investigator develops new algorithms for extracting and visualizing network structure, particularly the existence of communities in networks and structural properties related to network resilience, such as path counts and centrality measures. A knowledge of the structure of networks of acquaintance is crucial to the 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. A proper understanding of the nature and progress of epidemics is impossible without good network models. In this project the investigator determines what the structure of the networks in question is, and also models the effect of that structure on, among other things, the spread of information and disease. As well as enhancing basic understanding of these problems, the project points 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 able to suggest effective targets for immunization or education campaigns to slow disease spread. The new data resources and analysis techniques developed can be used to study other problems in which network-structured processes arise.

Agency
National Science Foundation (NSF)
Institute
Division of Mathematical Sciences (DMS)
Application #
0109086
Program Officer
Michael H. Steuerwalt
Project Start
Project End
Budget Start
2001-09-01
Budget End
2002-08-31
Support Year
Fiscal Year
2001
Total Cost
$71,052
Indirect Cost
Name
Santa Fe Institute
Department
Type
DUNS #
City
Santa Fe
State
NM
Country
United States
Zip Code
87501