Simple contagion processes underlie various phenomena on complex networks, such as the spread of diseases on social-contact networks and information in communication networks; understanding their dynamics and developing control mechanisms are key issues in numerous applications. The goals of this proposal are: (i) Developing methods to construct synthetic relational networks using partial and noisy data; (ii) Understanding the structure of these networks and the contagion processes, and especially important network properties and typical patterns that have an impact on the dynamics of contagion; (iii) Developing techniques to control the spread of contagion processes, and to detect, prevent and arrest cascading failures in coupled socio-technical networks; and (iv) Understanding the co-evolution between the networks and dynamics, and using this to refine their models, and the strategies to control them.
