Prospective Analysis of Large and Complex Partially Observed Temporal Social Networks

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DARPA project N66001-11-1-4183

Predictive Modeling of Patient State and Therapy Optimization Principal InvestigatorObradovic Zoran Abstract This project will develop and validate effective predictive modeling technology to achieve the

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Principal Investigator:

Prof. Obradovic Zoran


Prof. Emily Fox, University of Washington Statistics Department

Prof. Katya Scheinberg, Lehigh University Industrial and Systems Engineering Department.


The analysis of social networks often assumes a time invariant scenario, while in practice actor attributes and links in such networks evolve over time and are inextricably dependent on each other. In addition, the temporal graph is just partially observed, multiple kinds of links exist among actors, various actors have different temporal dynamics and environmental influence can be both positive and negative. This project is closely examining the hypothesis that a unified approach of jointly modeling these and related problems is beneficial for prospective analysis of large-scale partially observed temporal hypergraphs. Novel methods for analyzing large and evolving graphs developed on the project are evaluated on high impact applications related to predictive modeling of information networks, climate and human health.