Complex networks, consisting of interacting elements linked together with processing units, are ubiquitous across many disciplines of modern science and engineering. Examples include biochemical reaction networks, cellular networks, epidemiological networks, social networks, organizational networks, power distribution networks, as well as internet and mobile cellular networks. However, knowledge of the organizational structure and functional properties of most networks is very limited. Due to high complexity, elucidating the physical principles and structural mechanisms underlying interaction networks is a very difficult task, which requires collection and systematic analysis of large amounts of data. As a consequence, there is a general consensus that the development of a scientifically rigorous approach to studying interaction networks is urgently needed. The main goal of this research is to develop a general statistical signal processing methodology for model-based identification and analysis of complex nonlinear interaction networks from incomplete and noisy observations. The investigators study rigorous theoretical and computational techniques for estimating the structural and dynamic properties of interaction networks by state-of-the-art identification and model selection methodologies, and for studying network robustness via probabilistic sensitivity analysis. To achieve computational efficiency, a ?two-phase? approach to network identification is employed, guided by sensitivity analysis. This approach effectively exploits the fact that complex interaction networks are robust to most parameters. The objective of the first phase is to quickly estimate the values of the unknown network parameters from available measurements without much concern for their accuracy. The objective of the second phase is to use sensitivity analysis to accurately estimate the values of a small number of ?influential? parameters by using more informative observations of network behavior obtained by selective perturbations.
