Research on complex networks has important applications in a wide variety of social and technological systems, such as the Internet, online social networks, the electric grid, and other cyber-physical systems. Since critical societal functions are increasingly dependent on these complex networks, it is important to ensure that their performance and reliability are not degraded as the structure of the network evolves over time. This requires us to acquire a deeper understanding of the relationship between the structure and dynamics of these complex systems. Understanding the dynamics of complex networks is a challenging problem due to: (1) the cumbersome size and complexity of the systems (e.g. the Internet), (2) the nonexistence of a vantage point with complete information about the structure of the system (e.g. social networks), and (3) the structure of the system itself may be changing over time. In this talk, I present a novel theoretical foundation to analyze massive networks of dynamical elements using tools and techniques at the intersection of dynamical systems and control, probability, optimization, and graph theory. Alongside, I use real experimental data from technological networks (e.g. electric transmission networks and the Internet), as well as online social networks (e.g. Facebook and Twitter graphs) to validate theoretical predictions and build computational tools of practical interest.
Victor M. Preciado is the Raj and Neera Singh Assistant Professor of Electrical and Systems Engineering at the University of Pennsylvania. He received a PhD degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2008. His main research interests lie in the modeling, analysis, control and optimization of dynamical processes and strategic interactions in large-scale complex networks, with applications in social networks, electric power distribution, multi-agent systems, and biological networks.