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Center for Nonlinear Studies |
Complex Networks are pervasive in our society. The size and heterogeneity of these networks, their co-evolving nature and the technical difficulties in applying dimension reduction techniques commonly used to analyze physical systems makes the problem of reasoning prediction and control of these networks challenging. Recent quantitative changes in high performance and pervasive computing including faster machines, distributed sensors and service-oriented software have created new opportunities for collecting, integrating, analyzing and accessing information related to such large complex networks. The advances in network and information science that build on this new capability provide entirely new ways for reasoning and controlling these networks. Together, they enhance our ability to formulate, analyze and realize novel public policies pertaining to these complex networks. In this talk, I will summarize our ongoing integrated program to represent and reason about very large co-evolving social, technological, information and organization (STIO) networks. I will discuss our work in the context of epidemic processes in social and wireless networks. Understanding these epidemiological processes is of immense societal importance. Additionally they serve as excellent "model organisms" for developing a computational theory of co-evolving socio-technical networks. I will conclude by discussing directions for future research.
Host: Stephan Eidenbenz, CCS-3: INFORMATION SCIENCES, eidenben@lanl.gov